WRAP publications sorted by year

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  • Panyard, D., K. Kim, B. Darst, Y. Deming, X. Zhong, Y. Wu, H. Kang, C. Carlsson, S. Johnson, S. Asthana, C. Engelman, and Q. Lu. “Cerebrospinal Fluid Metabolomics Identifies 19 Brain-Related Phenotype associations.”. Communications Biology, Vol. 4, no. 1, 2021, p. 63.

    The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

    DOI: 10.1038/s42003-020-01583-z

    PubMed: 33437055


  • Bruno, D., K. Mueller, T. Betthauser, N. Chin, C. Engelman, B. Christian, R. Koscik, and S. Johnson. “Serial Position Effects in the Logical Memory Test: Loss of Primacy Predicts Amyloid positivity.”. Journal of Neuropsychology, 2020.

    Story recall is a frequently used neuropsychological test of episodic memory with clinical populations and for screening participants in drug trials for Alzheimer’s disease. However, it is unclear at this stage which underlying mechanisms confer the test its sensitivity. In this paper, we examined serial position effects, that is, better recall for items learned early and late on a list, in story recall, and their usefulness to predict early changes associated with neurodegenerative markers.

    DOI: 10.1111/jnp.12235

    PubMed: 33274833

  • Reyes, A., E. Kaestner, E. Edmonds, M. Christina, Z. Wang, D. Drane, V. Punia, R. Busch, B. Hermann, C. McDonald, and D. Alzheimer’s. “Diagnosing Cognitive Disorders in Older Adults With epilepsy.”. Epilepsia, 2020.

    To characterize the nature and prevalence of cognitive disorders in older adults with temporal lobe epilepsy (TLE) and compare their cognitive profiles to patients with amnestic mild cognitive impairment (ie, aMCI).

    DOI: 10.1111/epi.16780

    PubMed: 33258159

  • Luo, J., F. Agboola, E. Grant, C. Masters, M. Albert, S. Johnson, E. McDade, J. Vöglein, A. Fagan, T. Benzinger, P. Massoumzadeh, J. Hassenstab, R. Bateman, J. Morris, R. Perrin, J. Chhatwal, M. Jucker, B. Ghetti, C. Cruchaga, N. Graff-Radford, P. Schofield, H. Mori, and C. Xiong. “Sequence of Alzheimer Disease Biomarker Changes in Cognitively Normal Adults: A Cross-Sectional study.”. Neurology, Vol. 95, no. 23, 2020, pp. e3104-e3116.

    To determine the ordering of changes in Alzheimer disease (AD) biomarkers among cognitively normal individuals.

    DOI: 10.1212/WNL.0000000000010747

    PubMed: 32873693

  • Dougherty, R., E. Boots, J. Lindheimer, A. Stegner, R. Van, D. Edwards, C. Gallagher, C. Carlsson, H. Rowley, B. Bendlin, S. Asthana, B. Hermann, M. Sager, S. Johnson, O. Okonkwo, and D. Cook. “”. Brain Imaging and Behavior, Vol. 14, no. 4, 2020, pp. 1154-63.

    Patterns of decreased resting cerebral blood flow (CBF) within the inferior temporal gyri, angular gyri, and posterior cingulate are a feature of aging and Alzheimer’s disease (AD) and have shown to be predictive of cognitive decline among older adults. Fitness and physical activity are both associated with many indices of brain health and may positively influence CBF, however, the majority of research to date has examined these measures in isolation, leaving the potential independent associations unknown. The purpose of this study was to determine the unique contributions of fitness and physical activity when predicting CBF in cognitively healthy adults at risk for AD. One hundred participants (63% female) from the Wisconsin Registry for Alzheimer’s Prevention underwent a maximal exercise test, physical activity monitoring, and a 3-D arterial spin labeling magnetic resonance imaging scan. For the entire sample, fitness was significantly associated with CBF while accounting for physical activity, age, gender, APOE ε4, family history of AD, education, and handedness (p = .026). Further, fitness explained significantly more variance than the combined effect of the covariates on CBF (R2 change = .059; p = .047). These results appear to be gender dependent, our data suggest fitness level, independent of physical activity, is associated with greater CBF in regions that are known to decline with age and AD for female (p = .011), but not male participants.

    DOI: 10.1007/s11682-019-00068-w

    PubMed: 30852709

  • Pomponio, R., G. Erus, M. Habes, J. Doshi, D. Srinivasan, E. Mamourian, V. Bashyam, I. Nasrallah, T. Satterthwaite, Y. Fan, L. Launer, C. Masters, P. Maruff, C. Zhuo, H. Völzke, S. Johnson, J. Fripp, N. Koutsouleris, D. Wolf, R. Gur, R. Gur, J. Morris, M. Albert, H. Grabe, S. Resnick, R. Bryan, D. Wolk, R. Shinohara, H. Shou, and C. Davatzikos. “Harmonization of Large MRI Datasets for the Analysis of Brain Imaging Patterns Throughout the lifespan.”. NeuroImage, Vol. 208, 2020, p. 116450.

    As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

    DOI: 10.1016/j.neuroimage.2019.116450

    PubMed: 31821869

  • Betthauser, T., R. Koscik, E. Jonaitis, S. Allison, K. Cody, C. Erickson, H. Rowley, C. Stone, K. Mueller, L. Clark, C. Carlsson, N. Chin, S. Asthana, B. Christian, and S. Johnson. “Amyloid and Tau Imaging Biomarkers Explain Cognitive Decline from Late Middle-age.”. Brain : A Journal of Neurology, Vol. 143, no. 1, 2020, pp. 320-35.

    This study investigated differences in retrospective cognitive trajectories between amyloid and tau PET biomarker stratified groups in initially cognitively unimpaired participants sampled from the Wisconsin Registry for Alzheimer’s Prevention. One hundred and sixty-seven initially unimpaired individuals (baseline age 59 ± 6 years; 115 females) were stratified by elevated amyloid-β and tau status based on 11C-Pittsburgh compound B (PiB) and 18F-MK-6240 PET imaging. Mixed effects models were used to determine if longitudinal cognitive trajectories based on a composite of cognitive tests including memory and executive function differed between biomarker groups. Secondary analyses investigated group differences for a variety of cross-sectional health and cognitive tests, and associations between 18F-MK-6240, 11C-PiB, and age. A significant group × age interaction was observed with post hoc comparisons indicating that the group with both elevated amyloid and tau pathophysiology were declining approximately three times faster in retrospective cognition compared to those with just one or no elevated biomarkers. This result was robust against various thresholds and medial temporal lobe regions defining elevated tau. Participants were relatively healthy and mostly did not differ between biomarker groups in health factors at the beginning or end of study, or most cognitive measures at study entry. Analyses investigating association between age, MK-6240 and PiB indicated weak associations between age and 18F-MK-6240 in tangle-associated regions, which were negligible after adjusting for 11C-PiB. Strong associations, particularly in entorhinal cortex, hippocampus and amygdala, were observed between 18F-MK-6240 and global 11C-PiB in regions associated with Braak neurofibrillary tangle stages I-VI. These results suggest that the combination of pathological amyloid and tau is detrimental to cognitive decline in preclinical Alzheimer’s disease during late middle-age. Within the Alzheimer’s disease continuum, middle-age health factors likely do not greatly influence preclinical cognitive decline. Future studies in a larger preclinical sample are needed to determine if and to what extent individual contributions of amyloid and tau affect cognitive decline. 18F-MK-6240 shows promise as a sensitive biomarker for detecting neurofibrillary tangles in preclinical Alzheimer’s disease.

    DOI: 10.1093/brain/awz378

    PubMed: 31886494

  • Hunt, J., W. Buckingham, A. Kim, J. Oh, N. Vogt, E. Jonaitis, T. Hunt, M. Zuelsdorff, R. Powell, D. Norton, R. Rissman, S. Asthana, O. Okonkwo, S. Johnson, A. Kind, and B. Bendlin. “Association of Neighborhood-Level Disadvantage With Cerebral and Hippocampal Volume.”. JAMA Neurology, Vol. 77, no. 4, 2020, pp. 451-60.

    Identifying risk factors for brain atrophy during the aging process can help direct new preventive approaches for dementia and cognitive decline. The association of neighborhood socioeconomic disadvantage with brain volume in this context is not well known.

    DOI: 10.1001/jamaneurol.2019.4501

    PubMed: 31904767

  • Agelink, van, V. de, B. Schmand, J. Murre, J. Staaks, C. ANDI, and H. Huizenga. “The Factor Structure of Cognitive Functioning in Cognitively Healthy Participants: A Meta-Analysis and Meta-Analysis of Individual Participant Data.”. Neuropsychology Review, Vol. 30, no. 1, 2020, pp. 51-96.

    Many neuropsychologists are of the opinion that the multitude of cognitive tests may be grouped into a much smaller number of cognitive domains. However, there is little consensus on how many domains exist, what these domains are, nor on which cognitive tests belong to which domain. This incertitude can be solved by factor analysis, provided that the analysis includes a broad range of cognitive tests that have been administered to a very large number of people. In this article, two such factor analyses were performed, each combining multiple studies. However, because it was not possible to obtain complete multivariate data on more than the most common test variables in the field, not all possible domains were examined here. The first analysis was a factor meta-analysis of correlation matrices combining data of 60,398 healthy participants from 52 studies. Several models from the literature were fitted, of which a version based on the Cattell-Horn-Carroll (CHC) model was found to describe the correlations better than the others. The second analysis was a factor analysis of the Advanced Neuropsychological Diagnostics Infrastructure (ANDI) database, combining scores of 11,881 participants from 54 Dutch and Belgian studies not included in the first meta-analysis. Again, the model fit was better for the CHC model than for other models. Therefore, we conclude that the CHC model best characterizes both cognitive domains and which test belongs to each domain. Therefore, although originally developed in the intelligence literature, the CHC model deserves more attention in neuropsychology.

    DOI: 10.1007/s11065-019-09423-6

    PubMed: 32008158

  • Zemla, J., K. Cao, K. Mueller, and J. Austerweil. “SNAFU: The Semantic Network and Fluency Utility.”. Behavior Research Methods, Vol. 52, no. 4, 2020, pp. 1681-99.

    The verbal fluency task-listing words from a category or words that begin with a specific letter-is a common experimental paradigm that is used to diagnose memory impairments and to understand how we store and retrieve knowledge. Data from the verbal fluency task are analyzed in many different ways, often requiring manual coding that is time intensive and error-prone. Researchers have also used fluency data from groups or individuals to estimate semantic networks-latent representations of semantic memory that describe the relations between concepts-that further our understanding of how knowledge is encoded. However computational methods used to estimate networks are not standardized and can be difficult to implement, which has hindered widespread adoption. We present SNAFU: the Semantic Network and Fluency Utility, a tool for estimating networks from fluency data and automatizing traditional fluency analyses, including counting cluster switches and cluster sizes, intrusions, perseverations, and word frequencies. In this manuscript, we provide a primer on using the tool, illustrate its application by creating a semantic network for foods, and validate the tool by comparing results to trained human coders using multiple datasets.

    DOI: 10.3758/s13428-019-01343-w

    PubMed: 32128696

  • Rivera-Rivera, L., K. Cody, L. Eisenmenger, P. Cary, H. Rowley, C. Carlsson, S. Johnson, and K. Johnson. “”. Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 2020, p. 271678X20910302.

    Clinical evidence shows vascular factors may co-occur and complicate the expression of Alzheimer’s disease (AD); yet, the pathologic mechanisms and involvement of different compartments of the vascular network are not well understood. Diseases such as arteriosclerosis diminish vascular compliance and will lead to arterial stiffness, a well-established risk factor for cardiovascular morbidity. Arterial stiffness can be assessed using pulse wave velocity (PWV); however, this is usually done from carotid-to-femoral artery ratios. To probe the brain vasculature, intracranial PWV measures would be ideal. In this study, high temporal resolution 4D flow MRI was used to assess transcranial PWV in 160 subjects including AD, mild cognitive impairment (MCI), healthy controls, and healthy subjects with apolipoprotein ɛ4 positivity (APOE4+) and parental history of AD dementia (FH+). High temporal resolution imaging was achieved by high temporal binning of retrospectively gated data using a local-low rank approach. Significantly higher transcranial PWV in AD dementia and MCI subjects was found when compared to old-age-matched controls (AD vs. old-age-matched controls: P <0.001, AD vs. MCI: P = 0.029, MCI vs. old-age-matched controls P = 0.013). Furthermore, vascular changes were found in clinically healthy middle-age adults with APOE4+ and FH+ indicating significantly higher transcranial PWV compared to controls (P <0.001). DOI: 10.1177/0271678X20910302

    PubMed: 32169012

  • Mueller, K., D. Norton, R. Koscik, M. Morris, E. Jonaitis, L. Clark, T. Fields, S. Allison, S. Berman, S. Kraning, M. Zuelsdorff, O. Okonkwo, N. Chin, C. Carlsson, B. Bendlin, B. Hermann, and S. Johnson. “”. PloS One, Vol. 15, no. 4, 2020, p. e0221985.

    Studies have suggested associations between self-reported engagement in health behaviors and reduced risk of cognitive decline. Most studies explore these relationships using one health behavior, often cross-sectionally or with dementia as the outcome. In this study, we explored whether several individual self-reported health behaviors were associated with cognitive decline when considered simultaneously, using data from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), an Alzheimer’s disease risk-enriched cohort who were non-demented and in late midlife at baseline.

    DOI: 10.1371/journal.pone.0221985

    PubMed: 32324741

  • Raghavan, N., L. Dumitrescu, E. Mormino, E. Mahoney, A. Lee, Y. Gao, M. Bilgel, D. Goldstein, T. Harrison, C. Engelman, A. Saykin, C. Whelan, J. Liu, W. Jagust, M. Albert, S. Johnson, H. Yang, K. Johnson, P. Aisen, S. Resnick, R. Sperling, J. De, J. Schneider, D. Bennett, M. Schrag, B. Vardarajan, T. Hohman, R. Mayeux, and D. Alzheimer’s. “Association Between Common Variants in RBFOX1, an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer Disease.”. JAMA Neurology, 2020.

    Genetic studies of Alzheimer disease have focused on the clinical or pathologic diagnosis as the primary outcome, but little is known about the genetic basis of the preclinical phase of the disease.

    DOI: 10.1001/jamaneurol.2020.1760

    PubMed: 32568366

  • Bashyam, V., G. Erus, J. Doshi, M. Habes, I. Nasralah, M. Truelove-Hill, D. Srinivasan, L. Mamourian, R. Pomponio, Y. Fan, L. Launer, C. Masters, P. Maruff, C. Zhuo, H. Völzke, S. Johnson, J. Fripp, N. Koutsouleris, T. Satterthwaite, D. Wolf, R. Gur, R. Gur, J. Morris, M. Albert, H. Grabe, S. Resnick, R. Bryan, D. Wolk, H. Shou, and C. Davatzikos. “MRI Signatures of Brain Age and Disease over the Lifespan Based on a Deep Brain Network and 14 468 Individuals worldwide.”. Brain : A Journal of Neurology, Vol. 143, no. 7, 2020, pp. 2312-24.

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

    DOI: 10.1093/brain/awaa160

    PubMed: 32591831

  • Talamonti, D., R. Koscik, S. Johnson, and D. Bruno. “Temporal Contiguity and Ageing: The Role of Memory Organization in Cognitive decline.”. Journal of Neuropsychology, 2020.

    The temporal contiguity effect is the tendency to form associations between items presented in nearby study positions. In the present study, we explored whether temporal contiguity predicted conversion to cognitively unimpaired-declining (CUD) status from a baseline of unimpaired older adults. Data from 419 participants were drawn from the Wisconsin Registry of Alzheimer’s Prevention (WRAP) data set and analysed with binary logistic regressions. Temporal contiguity was calculated using the Rey Auditory Verbal Learning Test. Other predictors included age, years of education, sex, APOE-ε4 status, and other measures of memory recall. Lower temporal contiguity predicted conversion to CUD after accounting for covariates. These findings support the hypothesis that temporal organization in memory is related to cognitive decline and suggest that temporal contiguity may be used for studies of early detection.

    DOI: 10.1111/jnp.12219

    PubMed: 32652802

  • Mueller, K., R. Koscik, L. Du, D. Bruno, E. Jonaitis, A. Koscik, B. Christian, T. Betthauser, N. Chin, B. Hermann, and S. Johnson. “”. Cortex; A Journal Devoted to the Study of the Nervous System and Behavior, Vol. 131, 2020, pp. 137-50.

    Due to advances in the early detection of Alzheimer’s disease (AD) biomarkers including beta-amyloid (Aβ), neuropsychological measures that are sensitive to concurrent, subtle changes in cognition are critically needed. Story recall tasks have shown sensitivity to early memory declines in persons with mild cognitive impairment (MCI) and early stage dementia, as well as in persons with autosomal dominantly inherited AD up to 10 years prior to a dementia diagnosis. However, the evidence is inconclusive regarding relationships between evidence of Aβ and story recall measures. Because story recall tasks require the encoding and delayed retrieval of several lexical-semantic categories, such as proper names, verbs, and numerical expressions, and because lexical categories have been shown to be differentially impaired in persons with MCI, we focused on item-level analyses of lexical-semantic retrieval from a quintessential story recall task, Logical Memory from the Wechsler Memory Scale-Revised. Our objective was to investigate whether delayed recall of lexical categories (proper names, verbs and/or numerical expressions), as well as the traditional total score measure, was associated with “preclinical AD,” or cognitively unimpaired adults with positive Aβ deposition on positron emission tomography (PET) neuroimaging using Pittsburgh Compound B (PiB). We developed an item-level scoring system, in which we parsed items into lexical categories and examined the immediate and delayed recall of these lexical categories from 217 cognitively unimpaired participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP). We performed binary logistic regression models with story recall score as predictor and Aβ status (positive/negative) as the outcome. Using baseline Logical Memory data, proper names from delayed story recall were significantly associated with Aβ status, such that participants who recalled more proper names were less likely to be classified as PiB(+) (odds ratio = .58, p = .01). None of the other story recall variables, including total score, were associated with PiB status. Secondary analyses determined that immediate recall of proper names was not significantly associated with Aβ, suggesting a retrieval deficit rather than that of encoding. The present findings suggest that lexical semantic retrieval measures from existing story recall tasks may be sensitive to Aβ deposition, and may provide added utility to a widely-used, long-standing neuropsychological test for early detection of cognitive decline on the AD continuum.

    DOI: 10.1016/j.cortex.2020.07.008

    PubMed: 32861209


  • Racine, A., A. Merluzzi, N. Adluru, D. Norton, R. Koscik, L. Clark, S. Berman, C. Nicholas, S. Asthana, A. Alexander, K. Blennow, H. Zetterberg, W. Kim, V. Singh, C. Carlsson, B. Bendlin, and S. Johnson. “”. Brain Imaging and Behavior, Vol. 13, no. 1, 2019, pp. 41-52.

    Alzheimer’s disease (AD) is characterized by substantial neurodegeneration, including both cortical atrophy and loss of underlying white matter fiber tracts. Understanding longitudinal alterations to white matter may provide new insights into trajectories of brain change in both healthy aging and AD, and fluid biomarkers may be particularly useful in this effort. To examine this, 151 late-middle-aged participants enriched with risk for AD with at least one lumbar puncture and two diffusion tensor imaging (DTI) scans were selected for analysis from two large observational and longitudinally followed cohorts. Cerebrospinal fluid (CSF) was assayed for biomarkers of AD-specific pathology (phosphorylated-tau/Aβ42 ratio), axonal degeneration (neurofilament light chain protein, NFL), dendritic degeneration (neurogranin), and inflammation (chitinase-3-like protein 1, YKL-40). Linear mixed effects models were performed to test the hypothesis that biomarkers for AD, neurodegeneration, and inflammation, or two-year change in those biomarkers, would be associated with worse white matter health overall and/or progressively worsening white matter health over time. At baseline in the cingulum, phosphorylated-tau/Aβ42 was associated with higher mean diffusivity (MD) overall (intercept) and YKL-40 was associated with increases in MD over time. Two-year change in neurogranin was associated with higher mean diffusivity and lower fractional anisotropy overall (intercepts) across white matter in the entire brain and in the cingulum. These findings suggest that biomarkers for AD, neurodegeneration, and inflammation are potentially important indicators of declining white matter health in a cognitively healthy, late-middle-aged cohort.

    DOI: 10.1007/s11682-017-9732-9

    PubMed: 28600739

  • Zuelsdorff, M., R. Koscik, O. Okonkwo, P. Peppard, B. Hermann, M. Sager, S. Johnson, and C. Engelman. “Social Support and Verbal Interaction Are Differentially Associated With Cognitive Function in Midlife and Older age.”. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, Vol. 26, no. 2, 2019, pp. 144-60.

    Social engagement is associated with healthy aging and preserved cognition. Two dimensions of engagement, verbal interactions and perceived support, likely impact cognition via distinct mechanistic pathways. We explored the cognitive benefit of each construct among enrollees (N = 1,052, mean age = 60.2 years) in the Wisconsin Registry for Alzheimer’s Prevention study, who provide neuropsychological and sociobehavioral data at two-year intervals. Outcomes included six cognitive factor scores representing key domains of executive function and memory. Key predictors included self-reported perceived social support and weekly verbal interaction. Results indicated that after adjusting for lifestyle covariates, social support was positively associated with Speed and Flexibility and that verbal interactions were associated with Verbal Learning and Memory. These findings suggest that support, which may buffer stress, and verbal interaction, an accessible, aging-friendly form of environmental enrichment, are uniquely beneficial. Both are integral in the design of clinical and community interventions and programs that promote successful aging.

    DOI: 10.1080/13825585.2017.1414769

    PubMed: 29241403

  • Betthauser, T., K. Cody, M. Zammit, D. Murali, A. Converse, T. Barnhart, C. Stone, H. Rowley, S. Johnson, and B. Christian. “In Vivo Characterization and Quantification of Neurofibrillary Tau PET Radioligand 18F-MK-6240 in Humans from Alzheimer Disease Dementia to Young Controls.”. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine, Vol. 60, no. 1, 2019, pp. 93-99.

    Tau PET imaging has potential for elucidating changes in the deposition of neuropathological tau aggregates that are occurring during the progression of Alzheimer disease (AD). This work investigates in vivo kinetics, quantification strategies, and imaging characteristics of a novel tau PET radioligand 18F-MK-6240 in humans. Methods: Fifty-one individuals ranging from cognitively normal young controls to persons with dementia underwent T1-weighted MRI as well as 11C-PiB and 18F-MK-6240 PET imaging. PET data were coregistered to the MRI, and time-activity curves were extracted from regions of interest to assess 18F-MK-6240 kinetics. The pons and inferior cerebellum were investigated as potential reference regions. Reference tissue methods (Logan graphical analysis [LGA] and multilinear reference tissue method [MRTM2]) were investigated for quantification of 18F-MK-6240 distribution volume ratios (DVRs) in a subset of 19 participants. Stability of DVR methods was evaluated using truncated scan durations. SUV ratio (SUVR) estimates were compared with DVR estimates to determine the optimal timing window for SUVR analysis. Parametric SUVR images were used to identify regions of potential off-target binding and to compare binding patterns with neurofibrillary tau staging established in neuropathology literature. Results: SUVs in the pons and the inferior cerebellum indicated consistent clearance across all 51 subjects. LGA and MRTM2 DVR estimates were similar, with LGA slightly underestimating DVR compared with MRTM2. DVR estimates remained stable when truncating the scan duration to 60 min. SUVR determined 70-90 min after injection of 18F-MK-6240 indicated linearity near unity when compared with DVR estimates and minimized potential spill-in from uptake outside the brain. 18F-MK-6240 binding patterns in target regions were consistent with neuropathological neurofibrillary tau staging. Off-target binding regions included the ethmoid sinus, clivus, meninges, substantia nigra, but not the basal ganglia or choroid plexus. Conclusion:18F-MK-6240 is a promising PET radioligand for in vivo imaging of neurofibrillary tau aggregates in AD with minimal off-target binding in the human brain.

    DOI: 10.2967/jnumed.118.209650

    PubMed: 29777006

  • Hwang, S., N. Adluru, W. Kim, S. Johnson, B. Bendlin, and V. Singh. “”. Brain Connectivity, Vol. 9, no. 2, 2019, pp. 162-73.

    Characterizing Alzheimer’s disease (AD) at pre-clinical stages is crucial for initiating early treatment strategies. It is widely accepted that amyloid accumulation is a primary pathological event in AD. Also, loss of connectivity between brain regions is suspected of contributing to cognitive decline, but studies that test these associations using either local (i.e., individual edges) or global (i.e., modularity) connectivity measures may be limited. In this study, we utilized data acquired from 139 cognitively unimpaired participants. Sixteen gray matter (GM) regions known to be affected by AD were selected for analysis. For each of the 16 regions, the effect of amyloid burden, measured using Pittsburgh Compound B (PiB) positron emission tomography, on each of the 1761 brain network connections derived from diffusion tensor imaging (DTI) connecting 162 GM regions, was investigated. Applying our unique multiresolution statistical analysis called the Wavelet Connectivity Signature (WaCS), this study demonstrates the relationship between amyloid burden and structural brain connectivity as assessed with DTI. Our statistical analysis using WaCS shows that in 15 of 16 GM regions, statistically significant relationships between amyloid burden in those regions and structural connectivity networks were observed. After applying multiple testing correction, 10 unique structural brain connections were found to be significantly associated with amyloid accumulation. For 7 of those 10 network connections, the decrease in their network connection strength indexed by fractional anisotropy was, in turn, associated with lower cognitive function, providing evidence that AD-related structural connectivity loss is a correlate of cognitive decline.

    DOI: 10.1089/brain.2018.0590

    PubMed: 30255713

  • Koscik, R., E. Jonaitis, L. Clark, K. Mueller, S. Allison, C. Gleason, R. Chappell, B. Hermann, and S. Johnson. “”. Journal of the International Neuropsychological Society : JINS, Vol. 25, no. 1, 2019, pp. 1-14.

    A major challenge in cognitive aging is differentiating preclinical disease-related cognitive decline from changes associated with normal aging. Neuropsychological test authors typically publish single time-point norms, referred to here as unconditional reference values. However, detecting significant change requires longitudinal, or conditional reference values, created by modeling cognition as a function of prior performance. Our objectives were to create, depict, and examine preliminary validity of unconditional and conditional reference values for ages 40-75 years on neuropsychological tests.

    DOI: 10.1017/S1355617718000929

    PubMed: 30482257

  • Kim, W., A. Racine, N. Adluru, S. Hwang, K. Blennow, H. Zetterberg, C. Carlsson, S. Asthana, R. Koscik, S. Johnson, B. Bendlin, and V. Singh. “Cerebrospinal Fluid Biomarkers of Neurofibrillary Tangles and Synaptic Dysfunction Are Associated With Longitudinal Decline in White Matter Connectivity: A Multi-Resolution Graph analysis.”. NeuroImage. Clinical, Vol. 21, 2019, p. 101586.

    In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer’s disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia.

    DOI: 10.1016/j.nicl.2018.10.024

    PubMed: 30502079

  • Koscik, R., D. Norton, S. Allison, E. Jonaitis, L. Clark, K. Mueller, B. Hermann, C. Engelman, C. Gleason, M. Sager, R. Chappell, and S. Johnson. “”. Journal of the International Neuropsychological Society : JINS, Vol. 25, no. 2, 2019, pp. 119-33.

    Prior research has identified numerous genetic (including sex), education, health, and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find one model that best fits the observed data, risking interpretations that only the selected predictors are important. In reality, several predictor combinations may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates). In this study, we describe an alternative method, Information-Theoretic (IT) model averaging, and apply it to characterize a set of complex interactions in a longitudinal study on cognitive decline.

    DOI: 10.1017/S1355617718000954

    PubMed: 30522545

  • Darst, B., R. Koscik, K. Hogan, S. Johnson, and C. Engelman. “Longitudinal Plasma Metabolomics of Aging and sex.”. Aging, Vol. 11, no. 4, 2019, pp. 1262-8.

    Understanding how metabolites are longitudinally influenced by age and sex could facilitate the identification of metabolomic profiles and trajectories that indicate disease risk. We investigated the metabolomics of age and sex using longitudinal plasma samples from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a cohort of participants who were dementia free at enrollment. Metabolomic profiles were quantified for 2,344 fasting plasma samples among 1,212 participants, each with up to three study visits. Of 1,097 metabolites tested, 623 (56.8%) were associated with age and 695 (63.4%) with sex after correcting for multiple testing. Approximately twice as many metabolites were associated with age in stratified analyses of women versus men, and 68 metabolite trajectories significantly differed by sex, most notably including sphingolipids, which tended to increase in women and decrease in men with age. Using genome-wide genotyping, we also report the heritabilities of metabolites investigated, which ranged dramatically (0.2-99.2%); however, the median heritability of 36.2% suggests that many metabolites are highly influenced by a complex combination of genomic and environmental influences. These findings offer a more profound description of the aging process and may inform many new hypotheses regarding the role metabolites play in healthy and accelerated aging.

    DOI: 10.18632/aging.101837

    PubMed: 30799310

  • Darst, B., Q. Lu, S. Johnson, and C. Engelman. “”. Genetic Epidemiology, Vol. 43, no. 6, 2019, pp. 657-74.

    Although Alzheimer’s disease (AD) is highly heritable, genetic variants are known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multiomics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer’s Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fastings cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between carbamoyl-phosphate synthase 1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.

    DOI: 10.1002/gepi.22211

    PubMed: 31104335

  • Green-Harris, G., S. Coley, R. Koscik, N. Norris, S. Houston, M. Sager, S. Johnson, and D. Edwards. “”. Frontiers in Aging Neuroscience, Vol. 11, 2019, p. 125.

    DOI: 10.3389/fnagi.2019.00125

    PubMed: 31214014

  • Allison, S., R. Koscik, R. Cary, E. Jonaitis, H. Rowley, N. Chin, H. Zetterberg, K. Blennow, C. Carlsson, S. Asthana, B. Bendlin, and S. Johnson. “”. NeuroImage. Clinical, Vol. 23, 2019, p. 101895.

    Several neurodegeneration (N) metrics using structural MRI are used for the purpose of Alzheimer’s disease (AD)-related staging, including hippocampal volume, global atrophy, and an “AD signature” composite consisting of thickness or volumetric estimates derived from regions impacted early in AD. This study sought to determine if less user-intensive estimates of global atrophy and hippocampal volume were equivalent to a thickness-based AD signature from FreeSurfer for defining N across the AD continuum (i.e., individuals who are amyloid-positive (A+)).

    DOI: 10.1016/j.nicl.2019.101895

    PubMed: 31252287

  • O’Grady, J., D. Dean, K. Yang, C. Canda, S. Hoscheidt, E. Starks, A. Merluzzi, S. Hurley, N. Davenport, O. Okonkwo, R. Anderson, S. Asthana, S. Johnson, A. Alexander, and B. Bendlin. “Elevated Insulin and Insulin Resistance Are Associated With Altered Myelin in Cognitively Unimpaired Middle-Aged Adults.”. Obesity (Silver Spring, Md.), Vol. 27, no. 9, 2019, pp. 1464-71.

    Insulin regulates metabolism and influences neural health. Insulin resistance (IR) and type II diabetes have been identified as risk factors for Alzheimer disease (AD). Evidence has also suggested that myelinated white matter alterations may be involved in the pathophysiology of AD; however, it is unknown whether insulin or IR affect the underlying myelin microstructure. The relationships between insulin, IR, and myelin were examined, with the hypothesis that IR would be associated with reduced myelin.

    DOI: 10.1002/oby.22558

    PubMed: 31314172

  • Dumitrescu, L., L. Barnes, M. Thambisetty, G. Beecham, B. Kunkle, W. Bush, K. Gifford, L. Chibnik, S. Mukherjee, J. De, W. Kukull, P. Crane, S. Resnick, C. Keene, T. Montine, G. Schellenberg, Y. Deming, M. Chao, M. Huentelman, E. Martin, K. Hamilton-Nelson, L. Shaw, J. Trojanowski, E. Peskind, C. Cruchaga, M. Pericak-Vance, A. Goate, N. Cox, J. Haines, H. Zetterberg, K. Blennow, E. Larson, S. Johnson, M. Albert, D. Alzheimer’s, D. Bennett, J. Schneider, A. Jefferson, and T. Hohman. “”. Brain : A Journal of Neurology, Vol. 142, no. 9, 2019, pp. 2581-9.

    Autopsy measures of Alzheimer’s disease neuropathology have been leveraged as endophenotypes in previous genome-wide association studies (GWAS). However, despite evidence of sex differences in Alzheimer’s disease risk, sex-stratified models have not been incorporated into previous GWAS analyses. We looked for sex-specific genetic associations with Alzheimer’s disease endophenotypes from six brain bank data repositories. The pooled dataset included 2701 males and 3275 females, the majority of whom were diagnosed with Alzheimer’s disease at autopsy (70%). Sex-stratified GWAS were performed within each dataset and then meta-analysed. Loci that reached genome-wide significance (P < 5 × 10-8) in stratified models were further assessed for sex interactions. Additional analyses were performed in independent datasets leveraging cognitive, neuroimaging and CSF endophenotypes, along with age-at-onset data. Outside of the APOE region, one locus on chromosome 7 (rs34331204) showed a sex-specific association with neurofibrillary tangles among males (P = 2.5 × 10-8) but not females (P = 0.85, sex-interaction P = 2.9 × 10-4). In follow-up analyses, rs34331204 was also associated with hippocampal volume, executive function, and age-at-onset only among males. These results implicate a novel locus that confers male-specific protection from tau pathology and highlight the value of assessing genetic associations in a sex-specific manner. DOI: 10.1093/brain/awz206

    PubMed: 31497858

  • Xiong, C., J. Luo, F. Agboola, Y. Li, M. Albert, S. Johnson, R. Koscik, C. Masters, A. Soldan, V. Villemagne, Q. Li, E. McDade, A. Fagan, P. Massoumzadeh, T. Benzinger, J. Hassenstab, R. Bateman, J. Morris, and I. Dominantly. “, Vol. 15, no. 11, 2019, pp. 1448-57.

    Large longitudinal biomarkers database focusing on middle age is needed for Alzheimer’s disease (AD) prevention.

    DOI: 10.1016/j.jalz.2019.06.4955

    PubMed: 31506247

  • Guerrero, J., N. Adluru, B. Bendlin, H. Goldsmith, S. Schaefer, R. Davidson, S. Kecskemeti, H. Zhang, and A. Alexander. “Optimizing the Intrinsic Parallel Diffusivity in NODDI: An Extensive Empirical evaluation.”. PloS One, Vol. 14, no. 9, 2019, p. e0217118.

    NODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic parallel diffusivity (d∥) is set to 1.7 μm2⋅ms-1, though the effects of this assumption have not been extensively explored. This work investigates the optimality of d∥ = 1.7 μm2⋅ms-1 under varying imaging protocol, age groups, sex, and tissue type in comparison to other biologically plausible values of d∥.

    DOI: 10.1371/journal.pone.0217118

    PubMed: 31553719

  • Gaitán, J., E. Boots, R. Dougherty, J. Oh, Y. Ma, D. Edwards, B. Christian, D. Cook, and O. Okonkwo. “”. Brain Plasticity (Amsterdam, Netherlands), Vol. 5, no. 1, 2019, pp. 83-95.

    Aerobic exercise has been associated with reduced burden of brain and cognitive changes related to Alzheimer’s disease (AD). However, it is unknown whether exercise training in asymptomatic individuals harboring risk for AD improves outcomes associated with AD. We investigated the effect of 26 weeks of supervised aerobic treadmill exercise training on brain glucose metabolism and cognition among 23 late-middle-aged adults from a cohort enriched with familial and genetic risk of AD. They were randomized to Usual Physical Activity (PA) or Enhanced PA conditions. Usual PA received instruction about maintaining an active lifestyle. Enhanced PA completed a progressive exercise training program consisting of 3 sessions of treadmill walking per week for 26 weeks. By week seven, participants exercised at 70- 80% heart rate reserve for 50 minutes per session to achieve 150 minutes of moderate intensity activity per week in accordance with public health guidelines. Before and after the intervention, participants completed a graded treadmill test to assess VO2peak as a measure of cardiorespiratory fitness (CRF), wore an accelerometer to measure free-living PA, underwent 18F-fluorodeoxyglucose positron emission tomography imaging to assess brain glucose metabolism, and a neuropsychological battery to assess episodic memory and executive function. VO2peak increased, sedentary behavior decreased, and moderate-to-vigorous PA increased significantly in the Enhanced PA group as compared to Usual PA. Glucose metabolism in the posterior cingulate cortex (PCC) did not change significantly in Enhanced PA relative to Usual PA. However, change in PCC glucose metabolism correlated positively with change in VO2peak. Executive function, but not episodic memory, was significantly improved after Enhanced PA relative to Usual PA. Improvement in executive function correlated with increased VO2peak. Favorable CRF adaptation after 26 weeks of aerobic exercise training was associated with improvements in PCC glucose metabolism and executive function, important markers of AD.

    DOI: 10.3233/BPL-190093

    PubMed: 31970062

  • Hwang, S., R. Mehta, H. Kim, S. Johnson, and V. Singh. “Sampling-Free Uncertainty Estimation in Gated Recurrent Units With Applications to Normative Modeling in Neuroimaging.”. Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence, Vol. 2019, 2019.

    There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there are benefits to a system that is not only accurate but also has a sense for when it is not. Existing proposals center around Bayesian interpretations of modern deep architectures – these are effective but can often be computationally demanding. We show how classical ideas in the literature on exponential families on probabilistic networks provide an excellent starting point to derive uncertainty estimates in Gated Recurrent Units (GRU). Our proposal directly quantifies uncertainty deterministically, without the need for costly sampling-based estimation. We show that while uncertainty is quite useful by itself in computer vision and machine learning, we also demonstrate that it can play a key role in enabling statistical analysis with deep networks in neuroimaging studies with normative modeling methods. To our knowledge, this is the first result describing sampling-free uncertainty estimation for powerful sequential models such as GRUs.

    PubMed: 32405271

  • Zhen, X., R. Chakraborty, N. Vogt, B. Bendlin, and V. Singh. “Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data.”. Proceedings. IEEE International Conference on Computer Vision, Vol. 2019, 2019, pp. 10620-3.

    Efforts are underway to study ways via which the power of deep neural networks can be extended to non-standard data types such as structured data (e.g., graphs) or manifold-valued data (e.g., unit vectors or special matrices). Often, sizable empirical improvements are possible when the geometry of such data spaces are incorporated into the design of the model, architecture, and the algorithms. Motivated by neuroimaging applications, we study formulations where the data are sequential manifold-valued measurements. This case is common in brain imaging, where the samples correspond to symmetric positive definite matrices or orientation distribution functions. Instead of a recurrent model which poses computational/technical issues, and inspired by recent results showing the viability of dilated convolutional models for sequence prediction, we develop a dilated convolutional neural network architecture for this task. On the technical side, we show how the modules needed in our network can be derived while explicitly taking the Riemannian manifold structure into account. We show how the operations needed can leverage known results for calculating the weighted Fréchet Mean (wFM). Finally, we present scientific results for group difference analysis in Alzheimer’s disease (AD) where the groups are derived using AD pathology load: here the model finds several brain fiber bundles that are related to AD even when the subjects are all still cognitively healthy.

    DOI: 10.1109/iccv.2019.01072

    PubMed: 32405275

  • Hwang, S., Z. Tao, W. Kim, and V. Singh. “Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples With Applications to Neuroimaging.”. Proceedings. IEEE International Conference on Computer Vision, Vol. 2019, 2019, pp. 10691-00.

    We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provide a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer’s disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.

    DOI: 10.1109/iccv.2019.01079

    PubMed: 32405276





  • Boots, E., S. Schultz, J. Oh, J. Larson, D. Edwards, D. Cook, R. Koscik, M. Dowling, C. Gallagher, C. Carlsson, H. Rowley, B. Bendlin, A. LaRue, S. Asthana, B. Hermann, M. Sager, S. Johnson, and O. Okonkwo. “”. Brain Imaging and Behavior, Vol. 9, no. 3, 2015, pp. 639-49.

    Cardiorespiratory fitness (CRF) is an objective measure of habitual physical activity (PA), and has been linked to increased brain structure and cognition. The gold standard method for measuring CRF is graded exercise testing (GXT), but GXT is not feasible in many settings. The objective of this study was to examine whether a non-exercise estimate of CRF is related to gray matter (GM) volumes, white matter hyperintensities (WMH), cognition, objective and subjective memory function, and mood in a middle-aged cohort at risk for Alzheimer’s disease (AD). Three hundred and fifteen cognitively healthy adults (mean age =58.58 years) enrolled in the Wisconsin Registry for Alzheimer’s Prevention underwent structural MRI scanning, cognitive testing, anthropometric assessment, venipuncture for laboratory tests, and completed a self-reported PA questionnaire. A subset (n = 85) underwent maximal GXT. CRF was estimated using a previously validated equation incorporating sex, age, body-mass index, resting heart rate, and self-reported PA. Results indicated that the CRF estimate was significantly associated with GXT-derived peak oxygen consumption, validating its use as a non-exercise CRF measure in our sample. Support for this finding was seen in significant associations between the CRF estimate and several cardiovascular risk factors. Higher CRF was associated with greater GM volumes in several AD-relevant brain regions including the hippocampus, amygdala, precuneus, supramarginal gyrus, and rostral middle frontal gyrus. Increased CRF was also associated with lower WMH and better cognitive performance in Verbal Learning & Memory, Speed & Flexibility, and Visuospatial Ability. Lastly, CRF was negatively correlated with self- and informant-reported memory complaints, and depressive symptoms. Together, these findings suggest that habitual participation in physical activity may provide protection for brain structure and cognitive function, thereby decreasing future risk for AD.

    DOI: 10.1007/s11682-014-9325-9

    PubMed: 25319359

  • Nicholas, C., O. Okonkwo, B. Bendlin, J. Oh, S. Asthana, H. Rowley, B. Hermann, M. Sager, and S. Johnson. “Posteromedial Hyperactivation During Episodic Recognition Among People With Memory Decline: Findings from the WRAP study.”. Brain Imaging and Behavior, Vol. 9, no. 4, 2015, pp. 690-02.

    Episodic memory decline is one of the earliest preclinical symptoms of AD, and has been associated with an upregulation in the BOLD response in the prodromal stage (e.g. MCI) of AD. In a previous study, we observed upregulation in cognitively normal (CN) subjects with subclinical episodic memory decline compared to non-decliners. In light of this finding, we sought to determine if a separate cohort of Decliners will show increased brain activation compared to Stable subjects during episodic memory processing, and determine whether the BOLD effect was influenced by cerebral blood flow (CBF) or gray matter volume (GMV). Individuals were classified as a “Decliner” if scores on the Rey Auditory Verbal Learning Test (RAVLT) consistently fell ≥ 1.5 SD below expected intra- or inter-individual levels. FMRI was used to compare activation during a facial recognition memory task in 90 Stable (age = 59.1) and 34 Decliner (age = 62.1, SD = 5.9) CN middle-aged adults and 10 MCI patients (age = 72.1, SD = 9.4). Arterial spin labeling and anatomical T1 MRI were used to measure resting CBF and GMV, respectively. Stables and Decliners performed similarly on the episodic recognition memory task and significantly better than MCI patients. Compared to Stables, Decliners showed increased BOLD signal in the left precuneus on the episodic memory task that was not explained by CBF or GMV, familial AD risk factors, or neuropsychological measures. These findings suggest that subtle changes in the BOLD signal reflecting altered neural function may be a relatively early phenomenon associated with memory decline.

    DOI: 10.1007/s11682-014-9322-z

    PubMed: 25332108

  • Schultz, S., J. Larson, J. Oh, R. Koscik, M. Dowling, C. Gallagher, C. Carlsson, H. Rowley, B. Bendlin, S. Asthana, B. Hermann, S. Johnson, M. Sager, A. LaRue, and O. Okonkwo. “”. Brain Imaging and Behavior, Vol. 9, no. 4, 2015, pp. 729-36.

    This study tested the hypothesis that frequent participation in cognitively-stimulating activities, specifically those related to playing games and puzzles, is beneficial to brain health and cognition among middle-aged adults at increased risk for Alzheimer’s disease (AD). Three hundred twenty-nine cognitively normal, middle-aged adults (age range, 43.2-73.8 years) enrolled in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) participated in this study. They reported their current engagement in cognitive activities using a modified version of the Cognitive Activity Scale (CAS), underwent a structural MRI scan, and completed a comprehensive cognitive battery. FreeSurfer was used to derive gray matter (GM) volumes from AD-related regions of interest (ROIs), and composite measures of episodic memory and executive function were obtained from the cognitive tests. Covariate-adjusted least squares analyses were used to examine the association between the Games item on the CAS (CAS-Games) and both GM volumes and cognitive composites. Higher scores on CAS-Games were associated with greater GM volumes in several ROIs including the hippocampus, posterior cingulate, anterior cingulate, and middle frontal gyrus. Similarly, CAS-Games scores were positively associated with scores on the Immediate Memory, Verbal Learning & Memory, and Speed & Flexibility domains. These findings were not modified by known risk factors for AD. In addition, the Total score on the CAS was not as sensitive as CAS-Games to the examined brain and cognitive measures. For some individuals, participation in cognitive activities pertinent to game playing may help prevent AD by preserving brain structures and cognitive functions vulnerable to AD pathophysiology.

    DOI: 10.1007/s11682-014-9329-5

    PubMed: 25358750

  • Jones, J., D. Jackson, K. Chambers, K. Dabbs, D. Hsu, C. Stafstrom, M. Seidenberg, and B. Hermann. “Children With Epilepsy and Anxiety: Subcortical and Cortical differences.”. Epilepsia, Vol. 56, no. 2, 2015, pp. 283-90.

    Using a hypothesis-driven approach, subcortical and cortical regions implicated in anxiety disorders in the general population were examined in children with recent-onset epilepsy with versus without anxiety compared to controls. This study reports frequency of anxiety disorders while examining familial, clinical, and demographic variables associated with anxiety in children with epilepsy.

    DOI: 10.1111/epi.12832

    PubMed: 25580566

  • Almeida, R., S. Schultz, B. Austin, E. Boots, N. Dowling, C. Gleason, B. Bendlin, M. Sager, B. Hermann, H. Zetterberg, C. Carlsson, S. Johnson, S. Asthana, and O. Okonkwo. “Effect of Cognitive Reserve on Age-Related Changes in Cerebrospinal Fluid Biomarkers of Alzheimer Disease.”. JAMA Neurology, Vol. 72, no. 6, 2015, pp. 699-06.

    Although advancing age is the strongest risk factor for the development of symptomatic Alzheimer disease (AD), recent studies have shown that there are individual differences in susceptibility to age-related alterations in the biomarkers of AD pathophysiology.

    DOI: 10.1001/jamaneurol.2015.0098

    PubMed: 25893879

  • Jonaitis, E., R. Koscik, R. La, S. Johnson, B. Hermann, and M. Sager. “”. The Clinical Neuropsychologist, Vol. 29, no. 4, 2015, pp. 426-41.

    In the last five years, a consensus has developed that Alzheimer’s disease (AD) may begin years before overt cognitive impairment. Accordingly, the focus has shifted to identifying preclinical disease in order to match treatments to those most likely to benefit. Subtle cognitive changes, including reduced benefit from practice, may be one such preclinical sign. In this study, we explore cognitive aging trajectories within a large cohort of clinically intact late middle-aged adults.

    DOI: 10.1080/13854046.2015.1047407

    PubMed: 26012360

  • Mueller, K., R. Koscik, A. LaRue, L. Clark, B. Hermann, S. Johnson, and M. Sager. “”. Archives of Clinical Neuropsychology : The Official Journal of the National Academy of Neuropsychologists, Vol. 30, no. 5, 2015, pp. 448-57.

    This study examined the relationship between phonemic and semantic (category) verbal fluency and cognitive status in the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a longitudinal cohort enriched for family history of Alzheimer’s disease. Participants were 283 WRAP subjects (age 53.1[6.5] years at baseline); who had completed three waves of assessment, over ∼6 years and met psychometric criteria either for “cognitively healthy” (CH) or for psychometric amnestic mild cognitive impairment (aMCI) using an approach that did not consider fluency scores. CH and aMCI groups differed significantly on phonemic total scores, category total scores, phonemic switching, and category mean cluster size. These results suggest that measures of both phonemic and semantic fluency yield lower scores in persons with evidence of psychometric aMCI compared with those who are CH. Differences have not previously been reported in a group this young, and provide evidence for the importance of including multiple verbal fluency tests targeting preclinical Alzheimer’s disease.

    DOI: 10.1093/arclin/acv030

    PubMed: 26025231

  • Kim, W., N. Adluru, M. Chung, O. Okonkwo, S. Johnson, B. B, and V. Singh. “”. NeuroImage, Vol. 118, 2015, pp. 103-17.

    There is significant interest, both from basic and applied research perspectives, in understanding how structural/functional connectivity changes can explain behavioral symptoms and predict decline in neurodegenerative diseases such as Alzheimer’s disease (AD). The first step in most such analyses is to encode the connectivity information as a graph; then, one may perform statistical inference on various ‘global’ graph theoretic summary measures (e.g., modularity, graph diameter) and/or at the level of individual edges (or connections). For AD in particular, clear differences in connectivity at the dementia stage of the disease (relative to healthy controls) have been identified. Despite such findings, AD-related connectivity changes in preclinical disease remain poorly characterized. Such preclinical datasets are typically smaller and group differences are weaker. In this paper, we propose a new multi-resolution method for performing statistical analysis of connectivity networks/graphs derived from neuroimaging data. At the high level, the method occupies the middle ground between the two contrasts – that is, to analyze global graph summary measures (global) or connectivity strengths or correlations for individual edges similar to voxel based analysis (local). Instead, our strategy derives a Wavelet representation at each primitive (connection edge) which captures the graph context at multiple resolutions. We provide extensive empirical evidence of how this framework offers improved statistical power by analyzing two distinct AD datasets. Here, connectivity is derived from diffusion tensor magnetic resonance images by running a tractography routine. We first present results showing significant connectivity differences between AD patients and controls that were not evident using standard approaches. Later, we show results on populations that are not diagnosed with AD but have a positive family history risk of AD where our algorithm helps in identifying potentially subtle differences between patient groups. We also give an easy to deploy open source implementation of the algorithm for use within studies of connectivity in AD and other neurodegenerative disorders.

    DOI: 10.1016/j.neuroimage.2015.05.050

    PubMed: 26025289

  • Sprecher, K., B. Bendlin, A. Racine, O. Okonkwo, B. Christian, R. Koscik, M. Sager, S. Asthana, S. Johnson, and R. Benca. “Amyloid Burden Is Associated With Self-Reported Sleep in Nondemented Late Middle-Aged adults.”. Neurobiology of Aging, Vol. 36, no. 9, 2015, pp. 2568-76.

    Midlife may be an ideal window for intervention in Alzheimer’s disease (AD). To determine whether sleep is associated with early signs of AD neuropathology (amyloid deposition) in late midlife, we imaged brain amyloid deposits using positron emission tomography with [C-11]Pittsburgh Compound B (PiB), and assessed sleep with the Epworth Sleepiness Scale and the Medical Outcomes Study Sleep Scale in 98 cognitively healthy adults (aged 62.4 ± 5.7 years) from the Wisconsin Registry for Alzheimer’s Prevention. We used multiple regressions to test the extent to which sleep scores predicted regional amyloid burden. Participants reporting less adequate sleep, more sleep problems, and greater somnolence on the Medical Outcomes Study had greater amyloid burden in AD-sensitive brain regions (angular gyrus, frontal medial orbital cortex, cingulate gyrus, and precuneus). Amyloid was not associated with reported sleep amount, symptoms of sleep-disordered breathing, trouble falling asleep, or Epworth Sleepiness Scale. Poor sleep may be a risk factor for AD and a potential early marker of AD or target for preventative interventions in midlife.

    DOI: 10.1016/j.neurobiolaging.2015.05.004

    PubMed: 26059712

  • Boots, E., S. Schultz, R. Almeida, J. Oh, R. Koscik, M. Dowling, C. Gallagher, C. Carlsson, H. Rowley, B. Bendlin, S. Asthana, M. Sager, B. Hermann, S. Johnson, and O. Okonkwo. “”. Archives of Clinical Neuropsychology : The Official Journal of the National Academy of Neuropsychologists, Vol. 30, no. 7, 2015, pp. 634-42.

    Higher occupational attainment has previously been associated with increased Alzheimer’s disease (AD) neuropathology when individuals are matched for cognitive function, indicating occupation could provide cognitive reserve. We examined whether occupational complexity (OCC) associates with decreased hippocampal volume and increased whole-brain atrophy given comparable cognitive function in middle-aged adults at risk for AD. Participants (n = 323) underwent structural MRI, cognitive evaluation, and work history assessment. Three complexity ratings (work with data, people, and things) were obtained, averaged across up to 3 reported jobs, weighted by years per job, and summed to create a composite OCC rating. Greater OCC was associated with decreased hippocampal volume and increased whole-brain atrophy when matched for cognitive function; results remained substantively unchanged after adjusting for several demographic, AD risk, vascular, mental health, and socioeconomic characteristics. These findings suggest that, in people at risk for AD, OCC may confer resilience to the adverse effects of neuropathology on cognition.

    DOI: 10.1093/arclin/acv041

    PubMed: 26156334

  • Doherty, B., S. Schultz, J. Oh, R. Koscik, N. Dowling, T. Barnhart, D. Murali, C. Gallagher, C. Carlsson, B. Bendlin, A. LaRue, B. Hermann, H. Rowley, S. Asthana, M. Sager, B. Christian, S. Johnson, and O. Okonkwo. “, Vol. 1, no. 2, 2015, pp. 160-9.

    There is a growing interest in understanding how amyloid-β (Aβ) accumulation in preclinical Alzheimer’s disease relates to brain morphometric measures and cognition. Existing investigations in this area have been primarily conducted in older cognitively-normal (CN) individuals. Therefore, not much is known about the associations between Aβ burden, cortical thickness, and cognition in midlife. We examined this question in 109, CN, late-middle-aged adults (mean age=60.72±5.65 years) from the Wisconsin Registry for Alzheimer’s Prevention. They underwent Pittsburgh Compound B (PiB) and anatomical magnetic resonance (MR) imaging, and a comprehensive cognitive exam. Blinded visual rating of the PiB scans was used to classify the participants as Aβ+ or Aβ-. Cortical thickness measurements were derived from the MR images. The Aβ+ group exhibited significant thinning of the entorhinal cortex and accelerated age-associated thinning of the parahippocampal gyrus compared with the Aβ- group. The Aβ+ group also had numerically lower, but nonsignificant, test scores on all cognitive measures, and significantly faster age-associated cognitive decline on measures of Speed & Flexibility, Verbal Ability, and Visuospatial Ability. Our findings suggest that early Aβ aggregation is associated with deleterious changes in brain structure and cognitive function, even in midlife, and that the temporal lag between Aβ deposition and the inception of neurodegenerative/cognitive changes might be narrower than currently thought.

    DOI: 10.1016/j.dadm.2015.01.003

    PubMed: 26161436

  • Willette, A., B. Bendlin, E. Starks, A. Birdsill, S. Johnson, B. Christian, O. Okonkwo, R. La, B. Hermann, R. Koscik, E. Jonaitis, M. Sager, and S. Asthana. “Association of Insulin Resistance With Cerebral Glucose Uptake in Late Middle-Aged Adults at Risk for Alzheimer Disease.”. JAMA Neurology, Vol. 72, no. 9, 2015, pp. 1013-20.

    Converging evidence suggests that Alzheimer disease (AD) involves insulin signaling impairment. Patients with AD and individuals at risk for AD show reduced glucose metabolism, as indexed by fludeoxyglucose F 18-labeled positron emission tomography (FDG-PET).

    DOI: 10.1001/jamaneurol.2015.0613

    PubMed: 26214150

  • Schultz, S., E. Boots, R. Almeida, J. Oh, J. Einerson, C. Korcarz, D. Edwards, R. Koscik, M. Dowling, C. Gallagher, B. Bendlin, B. Christian, H. Zetterberg, K. Blennow, C. Carlsson, S. Asthana, B. Hermann, M. Sager, S. Johnson, J. Stein, and O. Okonkwo. “Cardiorespiratory Fitness Attenuates the Influence of Amyloid on Cognition.”. Journal of the International Neuropsychological Society : JINS, Vol. 21, no. 10, 2015, pp. 841-50.

    The aim of this study was to examine cross-sectionally whether higher cardiorespiratory fitness (CRF) might favorably modify amyloid-β (Aβ)-related decrements in cognition in a cohort of late-middle-aged adults at risk for Alzheimer’s disease (AD). Sixty-nine enrollees in the Wisconsin Registry for Alzheimer’s Prevention participated in this study. They completed a comprehensive neuropsychological exam, underwent 11C Pittsburgh Compound B (PiB)-PET imaging, and performed a graded treadmill exercise test to volitional exhaustion. Peak oxygen consumption (VO2peak) during the exercise test was used as the index of CRF. Forty-five participants also underwent lumbar puncture for collection of cerebrospinal fluid (CSF) samples, from which Aβ42 was immunoassayed. Covariate-adjusted regression analyses were used to test whether the association between Aβ and cognition was modified by CRF. There were significant VO2peak*PiB-PET interactions for Immediate Memory (p=.041) and Verbal Learning & Memory (p=.025). There were also significant VO2peak*CSF Aβ42 interactions for Immediate Memory (p<.001) and Verbal Learning & Memory (p<.001). Specifically, in the context of high Aβ burden, that is, increased PiB-PET binding or reduced CSF Aβ42, individuals with higher CRF exhibited significantly better cognition compared with individuals with lower CRF. In a late-middle-aged, at-risk cohort, higher CRF is associated with a diminution of Aβ-related effects on cognition. These findings suggest that exercise might play an important role in the prevention of AD. DOI: 10.1017/S1355617715000843

    PubMed: 26581795

  • Kim, W., B. Bendlin, M. Chung, S. Johnson, and V. Singh. “Statistical Inference Models for Image Datasets With Systematic Variations.”. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2015, 2015, pp. 4795-03.

    Statistical analysis of longitudinal or cross sectional brain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience. However, when there are systematic variations in the images due to parameter changes such as changes in the scanner protocol, hardware changes, or when combining data from multi-site studies, the statistical analysis becomes problematic. Motivated by this scenario, the goal of this paper is to develop a unified statistical solution to the problem of systematic variations in statistical image analysis. Based in part on recent literature in harmonic analysis on diffusion maps, we propose an algorithm which compares operators that are resilient to the systematic variations. These operators are derived from the empirical measurements of the image data and provide an efficient surrogate to capturing the actual changes across images. We also establish a connection between our method to the design of wavelets in non-Euclidean space. To evaluate the proposed ideas, we present various experimental results on detecting changes in simulations as well as show how the method offers improved statistical power in the analysis of real longitudinal PIB-PET imaging data acquired from participants at risk for Alzheimer’s disease (AD).

    DOI: 10.1109/CVPR.2015.7299112

    PubMed: 26989336

  • Kim, W., S. Ravi, S. Johnson, O. Okonkwo, and V. Singh. “On Statistical Analysis of Neuroimages With Imperfect Registration.”. Proceedings. IEEE International Conference on Computer Vision, Vol. 2015, 2015, pp. 666-74.

    A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine learning algorithms in a subsequent step. But errors in the underlying registration make this problematic, they either decrease the statistical power or make the follow-up inference tasks less effective/accurate. In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. By deriving a deformation invariant representation of the image, the downstream analysis can be made more robust as if one had access to a (hypothetical) far superior registration procedure. Our algorithm is based on recent work on scattering transform. Using this as a starting point, we show how results from harmonic analysis (especially, non-Euclidean wavelets) yields strategies for designing deformation and additive noise invariant representations of large 3-D brain image volumes. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal.

    DOI: 10.1109/ICCV.2015.83

    PubMed: 27042168

  • Hwang, S., M. Collins, S. Ravi, V. Ithapu, N. Adluru, S. Johnson, and V. Singh. “A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer.”. Proceedings. IEEE International Conference on Computer Vision, Vol. 2015, 2015, pp. 1841-9.

    Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation. Few other linear algebra problems have a more mature set of numerical routines available and many computer vision libraries leverage such tools extensively. However, the ability to call the underlying solver only as a “black box” can often become restrictive. Many ‘human in the loop’ settings in vision frequently exploit supervision from an expert, to the extent that the user can be considered a subroutine in the overall system. In other cases, there is additional domain knowledge, side or even partial information that one may want to incorporate within the formulation. In general, regularizing a (generalized) eigenvalue problem with such side information remains difficult. Motivated by these needs, this paper presents an optimization scheme to solve generalized eigenvalue problems (GEP) involving a (nonsmooth) regularizer. We start from an alternative formulation of GEP where the feasibility set of the model involves the Stiefel manifold. The core of this paper presents an end to end stochastic optimization scheme for the resultant problem. We show how this general algorithm enables improved statistical analysis of brain imaging data where the regularizer is derived from other ‘views’ of the disease pathology, involving clinical measurements and other image-derived representations.

    DOI: 10.1109/ICCV.2015.214

    PubMed: 27081374


  • Okonkwo, O., G. Xu, J. Oh, N. Dowling, C. Carlsson, C. Gallagher, A. Birdsill, M. Palotti, W. Wharton, B. Hermann, A. LaRue, B. Bendlin, H. Rowley, S. Asthana, M. Sager, and S. Johnson. “”. Cerebral Cortex (New York, N.Y. : 1991), Vol. 24, no. 4, 2014, pp. 978-88.

    Cerebral blood flow (CBF) provides an indication of the metabolic status of the cortex and may have utility in elucidating preclinical brain changes in persons at risk for Alzheimer’s disease (AD) and related diseases. In this study, we investigated CBF in 327 well-characterized adults including patients with AD (n = 28), patients with amnestic mild cognitive impairment (aMCI, n = 23), older cognitively normal (OCN, n = 24) adults, and asymptomatic middle-aged adults (n = 252) with and without a family history (FH) of AD. Compared with the asymptomatic cohort, AD patients displayed significant hypoperfusion in the precuneus, posterior cingulate, lateral parietal cortex, and the hippocampal region. Patients with aMCI exhibited a similar but less marked pattern of hypoperfusion. Perfusion deficits within the OCN adults were primarily localized to the inferior parietal lobules. Asymptomatic participants with a maternal FH of AD showed hypoperfusion in hippocampal and parietofrontal regions compared with those without a FH of AD or those with only a paternal FH of AD. These observations persisted when gray matter volume was included as a voxel-wise covariate. Our findings suggest that having a mother with AD might confer a particular risk for AD-related cerebral hypoperfusion in midlife. In addition, they provide further support for the potential utility of arterial spin labeling for the measurement of AD-related neurometabolic dysfunction, particularly in situations where [18F]fluorodeoxyglucose imaging is infeasible or clinically contraindicated.

    DOI: 10.1093/cercor/bhs381

    PubMed: 23236200

  • Ly, M., E. Canu, G. Xu, J. Oh, D. McLaren, N. Dowling, A. Alexander, M. Sager, S. Johnson, and B. Bendlin. “Midlife Measurements of White Matter Microstructure Predict Subsequent Regional White Matter Atrophy in Healthy adults.”. Human Brain Mapping, Vol. 35, no. 5, 2014, pp. 2044-54.

    Although age-related brain changes are becoming better understood, midlife patterns of change are still in need of characterization, and longitudinal studies are lacking. The aim of this study was to determine if baseline fractional anisotropy (FA), obtained from diffusion tensor imaging (DTI) predicts volume change over a 4-year interval.

    DOI: 10.1002/hbm.22311

    PubMed: 23861348

  • Birdsill, A., R. Koscik, E. Jonaitis, S. Johnson, O. Okonkwo, B. Hermann, A. Larue, M. Sager, and B. Bendlin. “”. Neurobiology of Aging, Vol. 35, no. 4, 2014, pp. 769-76.

    White matter hyperintensities (WMH) of presumed vascular origin, as seen on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging, are known to increase with age and are elevated in Alzheimer’s disease (AD). The cognitive implications of these common markers are not well understood. Previous research has primarily focused on global measures of WMH burden and broad localizations that contain multiple white matter tracts. The aims of this study were to determine the pattern of WMH accumulation with age, risk for AD, and the relationship with cognitive function utilizing a voxel-wise analysis capable of identifying specific white matter regions. A total of 349 participants underwent T1-weighted and high-resolution T2-weighted fluid attenuated inversion recovery magnetic resonance imaging and neuropsychological testing. Increasing age and lower cognitive speed and flexibility (a component of executive function), were both significantly associated with regional WMH throughout the brain. When age was controlled, lower cognitive speed and flexibility was independently associated with WMH in the superior corona radiata. Apolipoprotein E ε4 and parental family history of AD were not associated with higher burden of WMH. The results contribute to a larger body of literature suggesting that white matter measures are linked with processing speed, and illustrate the utility of voxel-wise analysis in understanding the effect of lesion location on cognitive function.

    DOI: 10.1016/j.neurobiolaging.2013.10.072

    PubMed: 24199958

  • Johnson, S., B. Christian, O. Okonkwo, J. Oh, S. Harding, G. Xu, A. Hillmer, D. Wooten, D. Murali, T. Barnhart, L. Hall, A. Racine, W. Klunk, C. Mathis, B. Bendlin, C. Gallagher, C. Carlsson, H. Rowley, B. Hermann, N. Dowling, S. Asthana, and M. Sager. “”. Neurobiology of Aging, Vol. 35, no. 3, 2014, pp. 576-84.

    To determine the relationship between amyloid burden and neural function in healthy adults at risk for Alzheimer’s Disease (AD), we used multimodal imaging with [C-11]Pittsburgh compound B positron emission tomography, [F-18]fluorodeoxyglucose, positron emission tomography , and magnetic resonance imaging, together with cognitive measurement in 201 subjects (mean age, 60.1 years; range, 46-73 years) from the Wisconsin Registry for Alzheimer’s Prevention. Using a qualitative rating, 18% of the samples were strongly positive Beta-amyloid (Aβ+), 41% indeterminate (Aβi), and 41% negative (Aβ-). Aβ+ was associated with older age, female sex, and showed trends for maternal family history of AD and APOE4. Relative to the Aβ- group, Aβ+ and Aβi participants had increased glucose metabolism in the bilateral thalamus; Aβ+ participants also had increased metabolism in the bilateral superior temporal gyrus. Aβ+ participants exhibited increased gray matter in the lateral parietal lobe bilaterally relative to the Aβ- group, and no areas of significant atrophy. Cognitive performance and self report cognitive and affective symptoms did not differ between groups. Amyloid burden can be identified in adults at a mean age of 60 years and is accompanied by glucometabolic increases in specific areas, but not atrophy or cognitive loss. This asymptomatic stage may be an opportune window for intervention to prevent progression to symptomatic AD.

    DOI: 10.1016/j.neurobiolaging.2013.09.028

    PubMed: 24269021

  • Engelman, C., R. Koscik, E. Jonaitis, B. Hermann, R. La, and M. Sager. “”. Neurobiology of Aging, Vol. 35, no. 6, 2014, pp. 1252-4.

    Recent studies have found an association between a variant in triggering receptor expressed on myeloid cells 2 (TREM2) (rs75932628-T) and both Alzheimer’s disease (AD) and cognitive function in individuals aged 80-100 years. The role of TREM2 in younger, asymptomatic individuals is unknown. We examined this variant in 1148 participants from the Wisconsin Registry for Alzheimer’s Prevention, a longitudinal study of middle-aged adults enriched for a parental history of AD. Thirteen individuals carried the T risk allele. Carriers were more likely to have a parental history of AD (100% of carriers vs. 70% of noncarriers; p = 0.01) and, among the parental history subset, families with a TREM2 carrier had a younger maternal age of AD onset than noncarriers (67.9 vs. 75.6 years; p = 0.03). There was no significant association between TREM2 carrier status and cognitive function or decline. In conclusion, the association between TREM2 and both parental history of AD and younger maternal age of AD onset provide additional support for the role of TREM2 in AD and illustrate the importance of considering family history in AD study design.

    DOI: 10.1016/j.neurobiolaging.2013.11.013

    PubMed: 24378087

  • Ithapu, V., V. Singh, C. Lindner, B. Austin, C. Hinrichs, C. Carlsson, B. Bendlin, and S. Johnson. “”. Human Brain Mapping, Vol. 35, no. 8, 2014, pp. 4219-35.

    Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer’s disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies.

    DOI: 10.1002/hbm.22472

    PubMed: 24510744

  • Koscik, R., R. La, E. Jonaitis, O. Okonkwo, S. Johnson, B. Bendlin, B. Hermann, and M. Sager. “”. Dementia and Geriatric Cognitive Disorders, Vol. 38, no. 1-2, 2014, pp. 16-30.

    It is difficult to reliably detect the earliest signs of Alzheimer’s disease (AD)-associated cognitive impairment. Our aim was to compare 3 psychometric methods of identifying amnestic mild cognitive impairment (aMCI) in a middle-aged longitudinal cohort enriched for AD risk.

    DOI: 10.1159/000355682

    PubMed: 24556849

  • Okonkwo, O., J. Oh, R. Koscik, E. Jonaitis, C. Cleary, N. Dowling, B. Bendlin, A. Larue, B. Hermann, T. Barnhart, D. Murali, H. Rowley, C. Carlsson, C. Gallagher, S. Asthana, M. Sager, B. Christian, and S. Johnson. “”. Journal of the International Neuropsychological Society : JINS, Vol. 20, no. 4, 2014, pp. 422-33.

    The relative influence of amyloid burden, neuronal structure and function, and prior cognitive performance on prospective memory decline among asymptomatic late middle-aged individuals at risk for Alzheimer’s disease (AD) is currently unknown. We investigated this using longitudinal cognitive data from 122 middle-aged adults (21 “Decliners” and 101 “Stables”) enrolled in the Wisconsin Registry for Alzheimer’s Prevention who underwent multimodality neuroimaging [11C-Pittsburgh Compound B (PiB), 18F-fluorodeoxyglucose (FDG), and structural/functional magnetic resonance imaging (fMRI)] 5.7 ± 1.4 years (range = 2.9-8.9) after their baseline cognitive assessment. Covariate-adjusted regression analyses revealed that the only imaging measure that significantly distinguished Decliners from Stables (p = .027) was a Neuronal Function composite derived from FDG and fMRI. In contrast, several cognitive measures, especially those that tap episodic memory, significantly distinguished the groups (p’s<.05). Complementary receiver operating characteristic curve analyses identified the Brief Visuospatial Memory Test-Revised (BVMT-R) Total (.82 ± .05, p < .001), the BVMT-R Delayed Recall (.73 ± .06, p = .001), and the Reading subtest from the Wide-Range Achievement Test-III (.72 ± .06, p = .002) as the top three measures that best discriminated the groups. These findings suggest that early memory test performance might serve a more clinically pivotal role in forecasting future cognitive course than is currently presumed. DOI: 10.1017/S1355617714000113

    PubMed: 24621494

  • Jackson, D., J. Lin, K. Chambers, A. Kessler-Jones, J. Jones, D. Hsu, C. Stafstrom, M. Seidenberg, and B. Hermann. “Birth Weight and Cognition in Children With epilepsy.”. Epilepsia, Vol. 55, no. 6, 2014, pp. 901-8.

    Birth weight is an important indicator of prenatal environment, and subtle variations of birth weight within the normal range have been associated with differential risk for cognitive and behavioral problems. Therefore, we aimed to determine if there are differences in birth weight between full-term children with uncomplicated new/recent-onset epilepsies and typically developing healthy controls. We further examined the relationships between birth weight and childhood/adolescent cognition, behavior, and academic achievement.

    DOI: 10.1111/epi.12622

    PubMed: 24735169

  • Racine, A., N. Adluru, A. Alexander, B. Christian, O. Okonkwo, J. Oh, C. Cleary, A. Birdsill, A. Hillmer, D. Murali, T. Barnhart, C. Gallagher, C. Carlsson, H. Rowley, N. Dowling, S. Asthana, M. Sager, B. Bendlin, and S. Johnson. “”. NeuroImage. Clinical, Vol. 4, 2014, pp. 604-14.

    Some cognitively healthy individuals develop brain amyloid accumulation, suggestive of incipient Alzheimer’s disease (AD), but the effect of amyloid on other potentially informative imaging modalities, such as Diffusion Tensor Imaging (DTI), in characterizing brain changes in preclinical AD requires further exploration. In this study, a sample (N = 139, mean age 60.6, range 46 to 71) from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a cohort enriched for AD risk factors, was recruited for a multimodal imaging investigation that included DTI and [C-11]Pittsburgh Compound B (PiB) positron emission tomography (PET). Participants were grouped as amyloid positive (Aβ+), amyloid indeterminate (Aβi), or amyloid negative (Aβ-) based on the amount and pattern of amyloid deposition. Regional voxel-wise analyses of four DTI metrics, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), and radial diffusivity (Dr), were performed based on amyloid grouping. Three regions of interest (ROIs), the cingulum adjacent to the corpus callosum, hippocampal cingulum, and lateral fornix, were selected based on their involvement in the early stages of AD. Voxel-wise analysis revealed higher FA among Aβ+ compared to Aβ- in all three ROIs and in Aβi compared to Aβ- in the cingulum adjacent to the corpus callosum. Follow-up exploratory whole-brain analyses were consistent with the ROI findings, revealing multiple regions where higher FA was associated with greater amyloid. Lower fronto-lateral gray matter MD was associated with higher amyloid burden. Further investigation showed a negative correlation between MD and PiB signal, suggesting that Aβ accumulation impairs diffusion. Interestingly, these findings in a largely presymptomatic sample are in contradistinction to relationships reported in the literature in symptomatic disease stages of Mild Cognitive Impairment and AD, which usually show higher MD and lower FA. Together with analyses showing that cognitive function in these participants is not associated with any of the four DTI metrics, the present results suggest an early relationship between PiB and DTI, which may be a meaningful indicator of the initiating or compensatory mechanisms of AD prior to cognitive decline.

    DOI: 10.1016/j.nicl.2014.02.001

    PubMed: 24936411

  • Adluru, N., D. Destiche, S. Lu, S. Doran, A. Birdsill, K. Melah, O. Okonkwo, A. Alexander, N. Dowling, S. Johnson, M. Sager, and B. Bendlin. “”. NeuroImage. Clinical, Vol. 4, 2014, pp. 730-42.

    Little is still known about the effects of risk factors for Alzheimer’s disease (AD) on white matter microstructure in cognitively healthy adults. The purpose of this cross-sectional study was to assess the effect of two well-known risk factors for AD, parental family history and APOE4 genotype.

    DOI: 10.1016/j.nicl.2014.04.008

    PubMed: 24936424

  • Mielke, M., N. Haughey, V. Bandaru, H. Zetterberg, K. Blennow, U. Andreasson, S. Johnson, C. Gleason, H. Blazel, L. Puglielli, M. Sager, S. Asthana, and C. Carlsson. “”. Neurobiology of Aging, Vol. 35, no. 11, 2014, pp. 2486-94.

    Cellular studies suggest sphingolipids may cause or accelerate amyloid-beta (Aβ) and tau pathology but in vivo human studies are lacking. We determined cerebrospinal fluid levels of sphingolipids (ceramides and sphingomyelins), amyloid-beta (Aβ1-42, AβX-38, AβX-40, and AβX-42) and tau (T-tau and p-tau181) in 91 cognitively normal individuals, aged 36-69 years, with a parental history of Alzheimer’s disease. The 18-carbon acyl chain length ceramide species was associated with AβX-38 (r = 0.312, p = 0.003), AβX-40 (r = 0.327, p = 0.002), and T-tau (r = 0.313, p = 0.003) but not with AβX-42 (r = 0.171, p = 0.106) or p-tau (r = 0.086, p = 0.418). All sphingomyelin species correlated (most p < 0.001) with all Aβ species and T-tau; many also correlated with p-tau. Results remained in regression models after controlling for age and APOE genotype. These results suggest in vivo relationships between cerebrospinal fluid ceramides and sphingomyelins and Aβ and tau levels in cognitively normal individuals at increased risk for Alzheimer's disease, indicating these sphingolipids may be associated with early pathogenesis. DOI: 10.1016/j.neurobiolaging.2014.05.019

    PubMed: 24952994

  • Okonkwo, O., S. Schultz, J. Oh, J. Larson, D. Edwards, D. Cook, R. Koscik, C. Gallagher, N. Dowling, C. Carlsson, B. Bendlin, A. LaRue, H. Rowley, B. Christian, S. Asthana, B. Hermann, S. Johnson, and M. Sager. “Physical Activity Attenuates Age-Related Biomarker Alterations in Preclinical AD.”. Neurology, Vol. 83, no. 19, 2014, pp. 1753-60.

    To examine whether engagement in physical activity might favorably alter the age-dependent evolution of Alzheimer disease (AD)-related brain and cognitive changes in a cohort of at-risk, late-middle-aged adults.

    DOI: 10.1212/WNL.0000000000000964

    PubMed: 25298312

  • Kim, H., N. Adluru, B. Bendlin, S. Johnson, B. Vemuri, and V. Singh. “Canonical Correlation Analysis on Riemannian Manifolds and Its Applications.”. Computer Vision - ECCV ... : ... European Conference on Computer Vision : Proceedings. European Conference on Computer Vision, Vol. 8690, 2014, pp. 251-67.

    Canonical correlation analysis (CCA) is a widely used statistical technique to capture correlations between two sets of multi-variate random variables and has found a multitude of applications in computer vision, medical imaging and machine learning. The classical formulation assumes that the data live in a pair of vector spaces which makes its use in certain important scientific domains problematic. For instance, the set of symmetric positive definite matrices (SPD), rotations and probability distributions, all belong to certain curved Riemannian manifolds where vector-space operations are in general not applicable. Analyzing the space of such data via the classical versions of inference models is rather sub-optimal. But perhaps more importantly, since the algorithms do not respect the underlying geometry of the data space, it is hard to provide statistical guarantees (if any) on the results. Using the space of SPD matrices as a concrete example, this paper gives a principled generalization of the well known CCA to the Riemannian setting. Our CCA algorithm operates on the product Riemannian manifold representing SPD matrix-valued fields to identify meaningful statistical relationships on the product Riemannian manifold. As a proof of principle, we present results on an Alzheimer’s disease (AD) study where the analysis task involves identifying correlations across diffusion tensor images (DTI) and Cauchy deformation tensor fields derived from T1-weighted magnetic resonance (MR) images.

    DOI: 10.1007/978-3-319-10605-2_17

    PubMed: 25317426

  • Kim, H., N. Adluru, M. Collins, M. Chung, B. Bendlin, S. Johnson, R. Davidson, and V. Singh. “Multivariate General Linear Models (MGLM) on Riemannian Manifolds With Applications to Statistical Analysis of Diffusion Weighted Images.”. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2014, 2014, pp. 2705-12.

    Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi , yi ) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xiR, against a manifold-valued variable, yi ∈ . We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression -a manifold-valued dependent variable against multiple independent variables, i.e., f : R
    → . Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on

    DOI: 10.1109/CVPR.2014.352

    PubMed: 25580070






  • Johnson, S., T. Schmitz, M. Trivedi, M. Ries, B. Torgerson, C. Carlsson, S. Asthana, B. Hermann, and M. Sager. “The Influence of Alzheimer Disease Family History and Apolipoprotein E Epsilon4 on Mesial Temporal Lobe activation.”. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, Vol. 26, no. 22, 2006, pp. 6069-76.

    First-degree family history of sporadic Alzheimer disease (AD) and the apolipoprotein E epsilon4 (APOE4) are risk factors for developing AD. Although the role of APOE4 in AD pathogenesis has been well studied, family history remains a rarely studied and poorly understood risk factor. Both putatively cause early brain changes before symptomatic disease, but the relative contribution of each to brain function is unknown. We examined 68 middle-aged participants with a parent diagnosed with AD [family history (+FH)] and 64 age- and education-matched controls without a first-degree family history of any dementia [no family history (-FH)]. All underwent cognitive testing, APOE genotyping, and a functional magnetic resonance imaging encoding task that required discrimination of novel items from previously learned items. A 2 x 2 factorial ANOVA (presence/absence of parental family history and presence/absence of the APOE4) was used to detect group effects. A greater response to novel items was detected in the mesial temporal lobe and fusiform gyrus bilaterally among persons without a first-degree family history of AD. In hippocampal areas, the -FH +epsilon4 group exhibited the greatest signal change, and the +FH +epsilon4 group exhibited the least. These findings indicate that FH of AD is an important predictor of hippocampal activation during encoding and that FH may modulate the effect of APOE4 in these middle-aged adults, suggesting that an as yet unspecified factor embodied in first-degree family history of AD is influencing the expression of APOE4 on brain function.

    DOI: 10.1523/JNEUROSCI.0959-06.2006

    PubMed: 16738250

  • Gleason, C., T. Schmitz, T. Hess, R. Koscik, M. Trivedi, M. Ries, C. Carlsson, M. Sager, S. Asthana, and S. Johnson. “Hormone Effects on FMRI and Cognitive Measures of Encoding: Importance of Hormone preparation.”. Neurology, Vol. 67, no. 11, 2006, pp. 2039-41.

    We compared fMRI and cognitive data from nine hormone therapy (HT)-naive women with data from women exposed to either opposed conjugated equine estrogens (CEE) (n = 10) or opposed estradiol (n = 4). Exposure to either form of HT was associated with healthier fMRI response; however, CEE-exposed women exhibited poorer memory performance than either HT-naive or estradiol-exposed subjects. These preliminary findings emphasize the need to characterize differential neural effects of various HTs.

    DOI: 10.1212/01.wnl.0000247277.81400.43

    PubMed: 17159116

  • Johnson, S., T. Schmitz, C. Moritz, M. Meyerand, H. Rowley, A. Alexander, K. Hansen, C. Gleason, C. Carlsson, M. Ries, S. Asthana, K. Chen, E. Reiman, and G. Alexander. “”. Neurobiology of Aging, Vol. 27, no. 11, 2006, pp. 1604-12.

    This study examined the functionality of the medial temporal lobe (MTL) and posterior cingulate (PC) in mild cognitive impairment amnestic type (MCI), a syndrome that puts patients at greater risk for developing Alzheimer disease (AD). Functional MRI (fMRI) was used to identify regions normally active during encoding of novel items and recognition of previously learned items in a reference group of 77 healthy young and middle-aged adults. The pattern of activation in this group guided further comparisons between 14 MCI subjects and 14 age-matched controls. The MCI patients exhibited less activity in the PC during recognition of previously learned items, and in the right hippocampus during encoding of novel items, despite comparable task performance to the controls. Reduced fMRI signal change in the MTL supports prior studies implicating the hippocampus for encoding new information. Reduced signal change in the PC converges with recent research on its role in recognition in normal adults as well as metabolic decline in people with genetic or cognitive risk for AD. Our results suggest that a change in function in the PC may account, in part, for memory recollection failure in AD.

    DOI: 10.1016/j.neurobiolaging.2005.09.017

    PubMed: 16226349

  • Trivedi, M., T. Schmitz, M. Ries, B. Torgerson, M. Sager, B. Hermann, S. Asthana, and S. Johnson. “”. BMC Medicine, Vol. 4, 2006, p. 1.

    The presence of the apolipoprotein E (APOE) epsilon4 allele is a major risk factor for the development of Alzheimer’s disease (AD), and has been associated with metabolic brain changes several years before the onset of typical AD symptoms. Functional MRI (fMRI) is a brain imaging technique that has been used to demonstrate hippocampal activation during measurement of episodic encoding, but the effect of the epsilon4 allele on hippocampal activation has not been firmly established.

    DOI: 10.1186/1741-7015-4-1

    PubMed: 16412236

  • Xu, G., D. McLaren, M. Ries, M. Fitzgerald, B. Bendlin, H. Rowley, M. Sager, C. Atwood, S. Asthana, and S. Johnson. “”. Brain : A Journal of Neurology, Vol. 132, no. Pt 2, 2009, pp. 383-91.

    First-degree family history (FH) of sporadic Alzheimer’s disease and the apolipoprotein E epsilon4 allele (APOE4) are risk factors for Alzheimer’s disease that may affect brain function prior to onset of clinical symptoms. In this functional MRI (fMRI) study, we used an episodic recognition task that required discrimination of previously viewed (PV) and novel (NV) faces to examine differences in blood oxygen level dependent (BOLD) signal due to risk factors in 74 middle-aged cognitively normal individuals. The group effects on this recognition task were tested with a 2 x 2 ANCOVA factorial design (+FH/-FH and +APOE4/-APOE4). There were significant APOE4 and FH effects in the left dorsal posterior cingulate cortex and precuneus, where decreased risk resulted in greater activity during recollection. Recognition performance was positively correlated with BOLD signal in the left posterior hippocampus, parahippocampal-retrosplenial gyrus and left superior frontal cortex regardless of risk factors. To examine condition-specific group effects, both the PV and NV faces were tested further in separate 2 x 2 ANCOVAs. Both models revealed an APOE effect, with the -APOE4 group showing stronger signal than the +APOE4 group in anterior cingulate cortices, while a FH effect was found in the dorsal cuneus and medial frontal cortices with the -FH group showing stronger signal than the +FH group. Finally, interactions between APOE4 and FH effects were found bilaterally in the fusiform gyrus. These results suggest that risk factors and cognitive performance each influence brain activity during recognition. The findings lend further support to the idea that functional brain changes may begin far in advance of symptomatic Alzheimer’s disease.

    DOI: 10.1093/brain/awn254

    PubMed: 18829694