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  • 1
    Online Resource
    Online Resource
    Stories from Hmong in STEMM
    Elsevier BV ; 2023
    In:  Trends in Genetics Vol. 39, No. 8 ( 2023-08), p. 587-592
    Details
    In: Trends in Genetics, Elsevier BV, Vol. 39, No. 8 ( 2023-08), p. 587-592
    Type of Medium: Online Resource
    ISSN: 0168-9525
    URL: Article
    DOI: 10.1016/j.tig.2023.04.007
    RVK:
    WA 15000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2010993-3
    SSG: 12
    SSG: 15,3
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  • 2
    Online Resource
    Online Resource
    P1‐462: AN MRI‐DERIVED DISEASE MARKER ASSOCIATES WITH CONVERSION TO MCI IN MIDDLE‐AGED ADULTS AT RISK FOR AD
    Wiley ; 2018
    In:  Alzheimer's & Dementia Vol. 14, No. 7S_Part_9 ( 2018-07)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 14, No. 7S_Part_9 ( 2018-07)
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Article
    DOI: 10.1016/j.jalz.2018.06.472
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2201940-6
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  • 3
    Online Resource
    Online Resource
    Elevated Insulin and Insulin Resistance are Associated with Altered Myelin in Cognitively Unimpaired Middle‐Aged Adults
    Wiley ; 2019
    In:  Obesity Vol. 27, No. 9 ( 2019-09), p. 1464-1471
    Details
    In: Obesity, Wiley, Vol. 27, No. 9 ( 2019-09), p. 1464-1471
    Abstract: 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. Methods Cognitively unimpaired adults enriched for risk factors for AD underwent multicomponent driven equilibrium single pulse observation of T1 and T2 imaging, a myelin‐sensitive neuroimaging technique. Linear regressions were used to test the relationship between homeostatic model assessment of IR, insulin, and myelin water fraction (MWF) as well as interactions with APOE ε4. Results Both IR and insulin level were associated with altered myelin content, wherein a significant negative association with MWF was observed in white matter regions and a positive association with MWF was observed in gray matter. Conclusions The results suggest that insulin and IR influence white matter myelination in a cognitively unimpaired population. Additional studies are needed to determine the extent to which this may contribute to cognitive decline or vulnerability to neurodegenerative disease.
    Type of Medium: Online Resource
    ISSN: 1930-7381 , 1930-739X
    URL: Issue
    URL: Article
    DOI: 10.1002/oby.v27.9
    DOI: 10.1002/oby.22558
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2027211-X
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  • 4
    Online Resource
    Online Resource
    Alzheimer’s pathologic change indexed by CSF B‐amyloid42 associates with longitudinal alterations in myelin content : Developing topics
    Wiley ; 2020
    In:  Alzheimer's & Dementia Vol. 16, No. S5 ( 2020-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 16, No. S5 ( 2020-12)
    Abstract: CSF Ab42 levels are associated with myelin content alterations in cognitively‐unimpaired adults (Dean et al, 2017), suggesting that myelin alterations may be an early feature of the Alzheimer’s disease (AD) pathophysiological cascade. We expand on these prior findings to examine longitudinal change in myelin content. Method 98 cognitively‐unimpaired (Table 1) participants from the Wisconsin Alzheimer’s Disease Research Center and Wisconsin Registry for Alzheimer’s Prevention underwent longitudinal multicomponent relaxometry (mcDESPOT) MRI, T2FLAIR MRI, and lumbar puncture (LP). All mcDESPOT MRI were normalized to MNI space using ANTs. White matter lesion volumes were extracted from T2FLAIR images using Lesion Segmentation Toolbox version 2.0.1 in SPM12. CSF was assayed for Ab42 using enzyme‐linked immunosorbent (INNOTEST) methods. 212 mcDESPOT‐derived myelin water fraction (MWF) maps, sensitive to myelin content, were generated for this analysis. MWF values were extracted from 6 white matter tracts affected in AD, and MWF values were averaged across regions to create a global index of myelin content at each timepoint. Linear mixed models were fit with longitudinal regional or global MWF as the outcome. All models included random intercepts and age‐related slopes with fixed effects of age (mean‐centered), sex, years between MRI and LP, white matter hyperintensity volume (log‐transformed) measured at first MRI, CSF Ab42 (mean‐centered) levels measured near first MRI, and Age*Ab42. All models were fitted in R (“lmer” package) version 3.6.0 and considered significant at unadjusted p 〈 .05. Result We observed widespread age associations with myelin content such that older age associated with lower global myelin content (Figure 1) and lower CSF Ab42 was associated with lower global myelin content (Figure 2). There was no significant interaction of age and CSF Ab42 on global myelin content. However, an age*Ab42 effect in the cingulum and uncinate fasciculus indicated that older age and lower CSF Ab42 was associated with decreased myelin content (Figure 3). Conclusion We previously found that lower CSF Ab42 was associated with lower myelin content among cognitively unimpaired adults. Here, we determined that for cognitively unimpaired middle‐aged adults, amyloid‐related longitudinal changes in myelin content also occur. Future studies will test if amyloid‐related myelin alterations may account for AD cognitive decline.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v16.S5
    DOI: 10.1002/alz.047629
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2201940-6
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  • 5
    Online Resource
    Online Resource
    Associations between gut microbial families and brain myelin in a human cohort
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S6 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S6 ( 2022-12)
    Abstract: We previously reported brain myelin (Dean et al, 2017) and gut microbial composition alterations (Vogt et al, 2017) in Alzheimer’s disease (AD). Prior animal studies suggest that myelin is impacted by experimentally altered gut microbiota (Keogh et al, 2021), suggesting that gut microbiota may be linked to brain myelination. However, little is yet known about the human gut microbiome and myelin alterations in the context of AD. Here, we examined the gut‐brain association in humans using biomarker and neuroimaging data. Method Amyloid positive (n=27) and negative (n=91) participants from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention were included based on completion of one MPnRAGE (Kecskemeti et al., 2018) scan and donation of one stool sample. Myelin was assessed using MPnRAGE‐derived quantitative R1 and myelin content in 9 white matter tracts were examined as primary outcomes (Table). Intestinal microbiota composition was characterized using 16S rRNA sequencing of fecal samples. QIIME2 was used to denoise and classify features. Phyloseq was used to filter rare taxa, compute relative abundances, and agglomerate at the family taxonomic rank, resulting in 35 microbial families used as primary predictors. Multiple regression was used to test associations between each microbial family and regional myelin content. Benjamini‐Hochberg false discovery rate (FDR) adjustment was applied post‐regression; results were considered significant at q 〈 .05. Result Microbial family Turicibacteraceae was positively associated with myelin indexed by R1 (Figure) in the forceps minor (b=.014, q =.037) and uncinate fasciculus (b=.011, q =.028). Across all white matter tracts, higher age was associated with lower R1. Interestingly, there was a significant Paraprevotellaceae ‐by‐amyloid effect in the superior longitudinal fasciculus (b= ‐.017, p =.031) and Clostridiaceae ‐by‐amyloid effect in the forceps major (b=.002, p =.037) though neither interaction survived FDR correction. Conclusion In both the uncinate fasciculus and forceps minor, higher levels of Turicibacteraceae were associated with greater myelin content. Turicibacteraceae were previously identified as less abundant among individuals with AD; the current study’s microbial associations with large white matter tracts that are altered in AD warrants further study. Future studies will test additional metrics of AD pathology to clarify gut microbe and brain relationships.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S6
    DOI: 10.1002/alz.069400
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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  • 6
    Online Resource
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    Neuronal networks are differentially affected in mutation carriers versus non‐carriers in autosomal dominant Alzheimer’s disease
    Wiley ; 2021
    In:  Alzheimer's & Dementia Vol. 17, No. S4 ( 2021-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S4 ( 2021-12)
    Abstract: Autosomal dominant Alzheimer’s disease (AD) is caused by known genetic mutations which results in the biochemical consequences that underlie the pathological basis of the disease, and a disease process driven by amyloid accumulation. Mutation carriage is characterized by substantial amyloid accumulation, and dementia onset at or around the age of parental dementia onset. Dementia onset is likely due to neurodegeneration, including loss of neuronal networks, although changes to structural connectivity remain incompletely characterized. Here, we report preliminary connection‐wise analysis of neuronal networks based on mutation and cognitive status, as well as estimated years to symptom onset (EYO), in autosomal dominant AD. Method Cross‐sectional diffusion tensor imaging (DTI) data from participants in the Dominantly Inherited Alzheimer Network were analyzed. DTI pre‐processing was performed using FSL and MRtrix3. Network node regions were identified based on the IIT‐Desikan gray matter atlas. Threshold‐free network‐based statistics (TFNBS) method was applied to test the effect of group as well as the linear interaction effects of the EYO and group on neuronal networks. The groups examined were mutation carrying AD (AD:MC), cognitively unimpaired mutation carriers (CU:MC), and cognitively unimpaired non‐carriers (CU:NC). Age and sex were used as nuisance variables in the linear models. Result Sample characteristics are shown in Table 1. Group level effects are shown in Figure 1. EYO by group interaction effects are shown in Figure 2. The names of the corresponding gray matter regions are shown in Table 2. Threshold‐free network‐based ‐statistic matrices for the interaction effects are shown in Figure 3. Conclusion AD:MC and CU:MC groups had higher weighted totals (a proxy of intra‐axonal cross‐sectional area of fiber bundles) in most of the neuronal connections. In addition, a similar increase in the weighted totals was observed as CU:MC group approached EYO. Increased cross‐sectional area may reflect loosening and shortening of fiber bundles with disease progression. Characterizing these regions among cognitively unimpaired individuals may inform the specific disease pathophysiology that act upon brain structures and lead to dementia. Future studies will examine these patterns among individuals with late onset AD and assess longitudinal relationships.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v17.S4
    DOI: 10.1002/alz.056287
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2201940-6
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  • 7
    Online Resource
    Online Resource
    Quantitative myelin imaging with MPnRAGE‐derived R1 mapping: Applications to Alzheimer’s disease
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S4 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S4 ( 2022-12)
    Abstract: Post‐mortem observations of Alzheimer’s disease (AD) brain suggest that developmental patterns of myelin and AD pathology are inversely related (Braak and Braak, 1996). Myelin injury and repair processes have also been implicated in the amyloid cascade hypothesis (Bartzokis, 2004; Depp et al., 2021). While diffusion‐weighted imaging has been widely applied to examine white matter abnormalities in AD, there remains a dearth of high resolution and high contrast imaging methods that allow for accurate and precise measurement of brain myelin in vivo. Here, we leveraged a novel imaging sequence—MPnRAGE (Kecskemeti et al., 2016; 2018)—to derive R1 maps, a myelin sensitive metric, and assessed the potential of R1 to identify myelin abnormalities in relation to cognitive impairment in AD. Method 791 cognitively unimpaired older adults, 30 with dementia, and 43 with MCI enrolled in the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center clinical core study underwent T1‐weighted (T1w) MPnRAGE with motion‐correction. T1w images and R1 maps were reconstructed at 1mm isotropic resolution. Preprocessing steps are outlined in Figure 1. As the T1w image and R1 map are inherently coregistered, no additional preprocessing was applied to the R1 map. Result A whole‐brain vertex‐wise cortical R1 map for a single participant and bilaterally averaged group mean parcellations for both cortical and WM R1 can be seen in Figure 2. Notably, densely‐myelinated regions are typically spared in AD, while thinly‐myelinated regions are vulnerable to AD pathology. In the cortex, sensory/motor regions exhibited the highest R1 (indicative of greater myelin content) and frontal/temporal regions had the lowest R1. In shallow WM, pericingulate WM exhibited the highest R1 and visual‐adjacent WM had the lowest R1. Participants with dementia and MCI had substantially reduced cortical adjacent WM R1 compared to unimpaired individuals (Figure 3). Conclusion This is the largest study to date to examine R1 maps among older adults with and without AD. These R1 maps are expected to facilitate analyses between myelin content and regional AD pathology accumulation as indexed by tau and amyloid PET, as well as testing the role of longitudinal myelin injury and repair in disease progression.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S4
    DOI: 10.1002/alz.061297
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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  • 8
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    Disaggregate data on Asian Americans — for science and scientists
    Springer Science and Business Media LLC ; 2022
    In:  Nature Vol. 603, No. 7899 ( 2022-03-03), p. 32-32
    Details
    In: Nature, Springer Science and Business Media LLC, Vol. 603, No. 7899 ( 2022-03-03), p. 32-32
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    URL: Article
    DOI: 10.1038/d41586-022-00539-z
    RVK:
    TA 1000
    RVK:
    UA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 9
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    Altered structural connectivity detected with dilated convolutional neural network analysis in the DIAN study and the Wisconsin Registry for Alzheimer’s Prevention
    Wiley ; 2021
    In:  Alzheimer's & Dementia Vol. 17, No. S4 ( 2021-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S4 ( 2021-12)
    Abstract: Dominantly inherited Alzheimer’s disease (DIAD) and late onset Alzheimer’s disease (LOAD) are characterized by the accumulation of amyloid pathology, and neurodegeneration which heralds the onset of dementia. Loss of structural connectivity prior to development of dementia may be measured using techniques that are sensitive to subtle neurodegeneration such as diffusion MRI (dMRI). We have previously shown the utility of deep learning (using a dilated convolutional neural network (DCNN) model) for analysis of sequential manifold‐valued data. Here, we apply this approach to dMRI data to test for loss of structural connectivity among mutation carriers who will develop DIAD, as well as among individuals who are at risk for LOAD due to amyloid pathology. Method Dataset 1 comprised 170 cognitively unimpaired participants from the Wisconsin Registry for Alzheimer’s Prevention study who underwent [11C]PiB‐PET to determine amyloid status, and dataset 2 comprised 440 participants in the Dominantly Inherited Alzheimer Network (DIAN) study. Demographics are shown in Table 2. Participants underwent diffusion weighted imaging which was processed using MRTrix3 and FSL toolkits. TractSeg was performed on both datasets to generate 50 white matter tracts, and tractometry was performed to generate mean representations of the tracts. Similar to previous work, we trained the dilated CNN model (Figure 1) using DTI values along the tracts for each group. The distance between the parameters of two models is treated as the difference between two groups. We performed permutation testing of 5000 runs on the distance to determine significant group differences within each dataset. Result The p‐values for each tract are shown in Table 1. Within the 50 fibers, we identified 14 tracts that differed by amyloid status, and 16 tracts that differed by mutation status. Across the two data sets, 9 tracts, e.g. Arcuate fascicle, and Cingulum, were found to be in common. Conclusion We demonstrate the ability to use the dilated CNN model to capture alterations along tract fibers among cognitively unimpaired individuals with preclinical amyloid as well as among mutation carriers who will develop DIAD. Longitudinal studies are needed to determine the temporal relationship between the accumulation of amyloid and neurodegeneration in the development of dementia.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v17.S4
    DOI: 10.1002/alz.054181
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2201940-6
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  • 10
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    Psychology Doctoral Program Admissions : What Master’s and Undergraduate-Level Students Need to Know
    SAGE Publications ; 2018
    In:  Teaching of Psychology Vol. 45, No. 1 ( 2018-01), p. 75-83
    Details
    In: Teaching of Psychology, SAGE Publications, Vol. 45, No. 1 ( 2018-01), p. 75-83
    Abstract: What do psychology doctorate programs require and prefer in their master’s level applicants? Do the programs value students’ graduate experiences during and postadmission? Doctoral programs’ ( n = 221) responses to an online survey showed that most required letters of recommendation, personal statements, Graduate Records Examination scores, and undergraduate grade point average. These credentials, interviewing skills, and student–mentor research match are crucial to admission decisions. However, clinical PhD, counseling PhD, clinical and counseling PsyD, practice subfields (e.g., school psychology), and research subfields (e.g., social psychology) evaluated differently 8 of the 26 credentials. Master’s-level applicants benefit more than bachelor’s-level applicants when beginning their doctoral work (e.g., having their master’s theses waived), but the advantages vary by subfields. Implications and recommendations for doctoral applicants are discussed.
    Type of Medium: Online Resource
    ISSN: 0098-6283 , 1532-8023
    URL: Article
    DOI: 10.1177/0098628317745453
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2022179-4
    SSG: 5,2
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