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Racine AM, Merluzzi AP, Adluru N, Norton D, Koscik RL, Clark LR, Berman SE, Nicholas CR, Asthana S, Alexander AL, Blennow K, Zetterberg H, Kim WH, Singh V, Carlsson CM, Bendlin BB, Johnson SC. Association of longitudinal white matter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer's disease in late-middle-aged adults. Brain Imaging Behav 2019; 13:41-52. [PMID: 28600739 PMCID: PMC5723250 DOI: 10.1007/s11682-017-9732-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
- Annie M Racine
- Neuroscience and Public Policy Program, University of Wisconsin, Madison, WI, USA
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Andrew P Merluzzi
- Neuroscience and Public Policy Program, University of Wisconsin, Madison, WI, USA
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Nagesh Adluru
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Derek Norton
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53792, USA
| | - Rebecca L Koscik
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Lindsay R Clark
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial VA Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sara E Berman
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Christopher R Nicholas
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial VA Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Sanjay Asthana
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial VA Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Andrew L Alexander
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53719, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Institute of Neurology, University College London, London, UK
| | - Won Hwa Kim
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53792, USA
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Vikas Singh
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53792, USA
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Cynthia M Carlsson
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial VA Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA
| | - Barbara B Bendlin
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA.
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA.
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial VA Hospital, 2500 Overlook Terrace, Madison, WI, 53705, USA.
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Hwang SJ, Adluru N, Kim WH, Johnson SC, Bendlin BB, Singh V. Associations Between Positron Emission Tomography Amyloid Pathology and Diffusion Tensor Imaging Brain Connectivity in Pre-Clinical Alzheimer's Disease. Brain Connect 2019; 9:162-173. [PMID: 30255713 DOI: 10.1089/brain.2018.0590] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
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.
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Affiliation(s)
- Seong Jae Hwang
- 1 Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin.,2 Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nagesh Adluru
- 3 Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, Wisconsin
| | - Won Hwa Kim
- 4 Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas
| | - Sterling C Johnson
- 2 Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,5 Geriatric Research Education and Clinical Center, William S. Middleton Veteran's Affairs Hospital, Madison, Wisconsin
| | - Barbara B Bendlin
- 2 Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,5 Geriatric Research Education and Clinical Center, William S. Middleton Veteran's Affairs Hospital, Madison, Wisconsin
| | - Vikas Singh
- 1 Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin.,2 Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,6 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
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Hwang SJ, Adluru N, Collins MD, Ravi SN, Bendlin BB, Johnson SC, Singh V. Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2016; 2016:2517-2525. [PMID: 27812274 DOI: 10.1109/cvpr.2016.276] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points - quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer's disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant's brain connectivity into the future.
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Affiliation(s)
- Seong Jae Hwang
- Dept. of Computer Sciences, University of Wisconsin - Madison
| | | | | | - Sathya N Ravi
- Dept. of Computer Sciences, University of Wisconsin - Madison
| | | | | | - Vikas Singh
- Dept. of Biostatistics and Med. Informatics, University of Wisconsin - Madison; Dept. of Computer Sciences, University of Wisconsin - Madison
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Characterization of Aspartate Kinase from Corynebacterium pekinense and the Critical Site of Arg169. Int J Mol Sci 2015; 16:28270-84. [PMID: 26633359 PMCID: PMC4691045 DOI: 10.3390/ijms161226098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 11/10/2015] [Accepted: 11/11/2015] [Indexed: 11/21/2022] Open
Abstract
Aspartate kinase (AK) is the key enzyme in the biosynthesis of aspartate-derived amino acids. Recombinant AK was efficiently purified and systematically characterized through analysis under optimal conditions combined with steady-state kinetics study. Homogeneous AK was predicted as a decamer with a molecular weight of ~48 kDa and a half-life of 4.5 h. The enzymatic activity was enhanced by ethanol and Ni2+. Moreover, steady-state kinetic study confirmed that AK is an allosteric enzyme, and its activity was inhibited by allosteric inhibitors, such as Lys, Met, and Thr. Theoretical results indicated the binding mode of AK and showed that Arg169 is an important residue in substrate binding, catalytic domain, and inhibitor binding. The values of the kinetic parameter Vmax of R169 mutants, namely, R169Y, R169P, R169D, and R169H AK, with l-aspartate as the substrate, were 4.71-, 2.25-, 2.57-, and 2.13-fold higher, respectively, than that of the wild-type AK. Furthermore, experimental and theoretical data showed that Arg169 formed a hydrogen bond with Glu92, which functions as the entrance gate. This study provides a basis to develop new enzymes and elucidate the corresponding amino acid production.
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Kim WH, Adluru N, Chung MK, Okonkwo OC, Johnson SC, B Bendlin B, Singh V. Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer's disease. Neuroimage 2015; 118:103-17. [PMID: 26025289 DOI: 10.1016/j.neuroimage.2015.05.050] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 04/02/2015] [Accepted: 05/18/2015] [Indexed: 11/28/2022] Open
Abstract
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.
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Affiliation(s)
- Won Hwa Kim
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA; Wisconsin Alzheimer's Disease Research Center, Madison, WI 53792, USA.
| | | | - Moo K Chung
- Department of Biostatistics & Med. Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Ozioma C Okonkwo
- William S. Middleton Veteran's Affairs Hospital, Madison, WI 53792, USA; Wisconsin Alzheimer's Disease Research Center, Madison, WI 53792, USA
| | - Sterling C Johnson
- William S. Middleton Veteran's Affairs Hospital, Madison, WI 53792, USA; Wisconsin Alzheimer's Disease Research Center, Madison, WI 53792, USA
| | - Barbara B Bendlin
- William S. Middleton Veteran's Affairs Hospital, Madison, WI 53792, USA; Wisconsin Alzheimer's Disease Research Center, Madison, WI 53792, USA
| | - Vikas Singh
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Biostatistics & Med. Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA; Wisconsin Alzheimer's Disease Research Center, Madison, WI 53792, USA.
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