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61 Network Segregation Predicts Processing Speed in the Cognitively Healthy Oldest-old
- Sara A Nolin, Mary E Faulkner, Paul Stewart, Leland Fleming, Stacy Merritt, Roxanne F Rezaei, Pradyumna K Bharadwaj, Mary Kathryn Franchetti, Daniel A Raichlen, Courtney J Jessup, Lloyd Edwards, G Alex Hishaw, Emily J Van Etten, Theodore P Trouard, David S Geldmacher, Virginia G Wadley, Noam Alperin, Eric C Porges, Adam J Woods, Ronald A Cohen, Bonnie E Levin, Tatjana Rundek, Gene E Alexander, Kristina M Visscher
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 367-368
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Objective:
Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.
Participants and Methods:Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.
Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.
Results:We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).
Conclusions:This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.
2 Higher White Matter Hyperintensity Load Adversely Affects Pre-Post Proximal Cognitive Training Performance in Healthy Older Adults
- Emanuel M Boutzoukas, Andrew O’Shea, Jessica N Kraft, Cheshire Hardcastle, Nicole D Evangelista, Hanna K Hausman, Alejandro Albizu, Emily J Van Etten, Pradyumna K Bharadwaj, Samantha G Smith, Hyun Song, Eric C Porges, Alex Hishaw, Steven T DeKosky, Samuel S Wu, Michael Marsiske, Gene E Alexander, Ronald Cohen, Adam J Woods
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 671-672
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Objective:
Cognitive training has shown promise for improving cognition in older adults. Aging involves a variety of neuroanatomical changes that may affect response to cognitive training. White matter hyperintensities (WMH) are one common age-related brain change, as evidenced by T2-weighted and Fluid Attenuated Inversion Recovery (FLAIR) MRI. WMH are associated with older age, suggestive of cerebral small vessel disease, and reflect decreased white matter integrity. Higher WMH load associates with reduced threshold for clinical expression of cognitive impairment and dementia. The effects of WMH on response to cognitive training interventions are relatively unknown. The current study assessed (a) proximal cognitive training performance following a 3-month randomized control trial and (b) the contribution of baseline whole-brain WMH load, defined as total lesion volume (TLV), on pre-post proximal training change.
Participants and Methods:Sixty-two healthy older adults ages 65-84 completed either adaptive cognitive training (CT; n=31) or educational training control (ET; n=31) interventions. Participants assigned to CT completed 20 hours of attention/processing speed training and 20 hours of working memory training delivered through commercially-available Posit Science BrainHQ. ET participants completed 40 hours of educational videos. All participants also underwent sham or active transcranial direct current stimulation (tDCS) as an adjunctive intervention, although not a variable of interest in the current study. Multimodal MRI scans were acquired during the baseline visit. T1- and T2-weighted FLAIR images were processed using the Lesion Segmentation Tool (LST) for SPM12. The Lesion Prediction Algorithm of LST automatically segmented brain tissue and calculated lesion maps. A lesion threshold of 0.30 was applied to calculate TLV. A log transformation was applied to TLV to normalize the distribution of WMH. Repeated-measures analysis of covariance (RM-ANCOVA) assessed pre/post change in proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures in the CT group compared to their ET counterparts, controlling for age, sex, years of education and tDCS group. Linear regression assessed the effect of TLV on post-intervention proximal composite and sub-composite, controlling for baseline performance, intervention assignment, age, sex, years of education, multisite scanner differences, estimated total intracranial volume, and binarized cardiovascular disease risk.
Results:RM-ANCOVA revealed two-way group*time interactions such that those assigned cognitive training demonstrated greater improvement on proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures compared to their ET counterparts. Multiple linear regression showed higher baseline TLV associated with lower pre-post change on Processing Speed Training sub-composite (ß = -0.19, p = 0.04) but not other composite measures.
Conclusions:These findings demonstrate the utility of cognitive training for improving postintervention proximal performance in older adults. Additionally, pre-post proximal processing speed training change appear to be particularly sensitive to white matter hyperintensity load versus working memory training change. These data suggest that TLV may serve as an important factor for consideration when planning processing speed-based cognitive training interventions for remediation of cognitive decline in older adults.
6 Adjunctive Transcranial Direct Current Stimulation and Cognitive Training Alters Default Mode and Frontoparietal Control Network Connectivity in Older Adults
- Hanna K Hausman, Jessica N Kraft, Cheshire Hardcastle, Nicole D Evangelista, Emanuel M Boutzoukas, Andrew O’Shea, Alejandro Albizu, Emily J Van Etten, Pradyumna K Bharadwaj, Hyun Song, Samantha G Smith, Eric S Porges, Georg A Hishaw, Samuel Wu, Steven DeKosky, Gene E Alexander, Michael Marsiske, Ronald A Cohen, Adam J Woods
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 675-676
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Objective:
Aging is associated with disruptions in functional connectivity within the default mode (DMN), frontoparietal control (FPCN), and cingulo-opercular (CON) resting-state networks. Greater within-network connectivity predicts better cognitive performance in older adults. Therefore, strengthening network connectivity, through targeted intervention strategies, may help prevent age-related cognitive decline or progression to dementia. Small studies have demonstrated synergistic effects of combining transcranial direct current stimulation (tDCS) and cognitive training (CT) on strengthening network connectivity; however, this association has yet to be rigorously tested on a large scale. The current study leverages longitudinal data from the first-ever Phase III clinical trial for tDCS to examine the efficacy of an adjunctive tDCS and CT intervention on modulating network connectivity in older adults.
Participants and Methods:This sample included 209 older adults (mean age = 71.6) from the Augmenting Cognitive Training in Older Adults multisite trial. Participants completed 40 hours of CT over 12 weeks, which included 8 attention, processing speed, and working memory tasks. Participants were randomized into active or sham stimulation groups, and tDCS was administered during CT daily for two weeks then weekly for 10 weeks. For both stimulation groups, two electrodes in saline-soaked 5x7 cm2 sponges were placed at F3 (cathode) and F4 (anode) using the 10-20 measurement system. The active group received 2mA of current for 20 minutes. The sham group received 2mA for 30 seconds, then no current for the remaining 20 minutes.
Participants underwent resting-state fMRI at baseline and post-intervention. CONN toolbox was used to preprocess imaging data and conduct region of interest (ROI-ROI) connectivity analyses. The Artifact Detection Toolbox, using intermediate settings, identified outlier volumes. Two participants were excluded for having greater than 50% of volumes flagged as outliers. ROI-ROI analyses modeled the interaction between tDCS group (active versus sham) and occasion (baseline connectivity versus postintervention connectivity) for the DMN, FPCN, and CON controlling for age, sex, education, site, and adherence.
Results:Compared to sham, the active group demonstrated ROI-ROI increases in functional connectivity within the DMN following intervention (left temporal to right temporal [T(202) = 2.78, pFDR < 0.05] and left temporal to right dorsal medial prefrontal cortex [T(202) = 2.74, pFDR < 0.05]. In contrast, compared to sham, the active group demonstrated ROI-ROI decreases in functional connectivity within the FPCN following intervention (left dorsal prefrontal cortex to left temporal [T(202) = -2.96, pFDR < 0.05] and left dorsal prefrontal cortex to left lateral prefrontal cortex [T(202) = -2.77, pFDR < 0.05]). There were no significant interactions detected for CON regions.
Conclusions:These findings (a) demonstrate the feasibility of modulating network connectivity using tDCS and CT and (b) provide important information regarding the pattern of connectivity changes occurring at these intervention parameters in older adults. Importantly, the active stimulation group showed increases in connectivity within the DMN (a network particularly vulnerable to aging and implicated in Alzheimer’s disease) but decreases in connectivity between left frontal and temporal FPCN regions. Future analyses from this trial will evaluate the association between these changes in connectivity and cognitive performance post-intervention and at a one-year timepoint.
9 Connecting memory and functional brain networks in older adults: a resting state fMRI study
- Jori L Waner, Hanna K Hausman, Jessica N Kraft, Cheshire Hardcastle, Nicole D Evangelista, Andrew O’Shea, Alejandro Albizu, Emanuel M Boutzoukas, Emily J Van Etten, Pradyumna K Bharadwaj, Hyun Song, Samantha G Smith, Steven T DeKosky, Georg A Hishaw, Samuel S Wu, Michael Marsiske, Ronald Cohen, Gene E Alexander, Eric C Porges, Adam J Woods
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 527-528
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Objective:
Nonpathological aging has been linked to decline in both verbal and visuospatial memory abilities in older adults. Disruptions in resting-state functional connectivity within well-characterized, higherorder cognitive brain networks have also been coupled with poorer memory functioning in healthy older adults and in older adults with dementia. However, there is a paucity of research on the association between higherorder functional connectivity and verbal and visuospatial memory performance in the older adult population. The current study examines the association between resting-state functional connectivity within the cingulo-opercular network (CON), frontoparietal control network (FPCN), and default mode network (DMN) and verbal and visuospatial learning and memory in a large sample of healthy older adults. We hypothesized that greater within-network CON and FPCN functional connectivity would be associated with better immediate verbal and visuospatial memory recall. Additionally, we predicted that within-network DMN functional connectivity would be associated with improvements in delayed verbal and visuospatial memory recall. This study helps to glean insight into whether within-network CON, FPCN, or DMN functional connectivity is associated with verbal and visuospatial memory abilities in later life.
Participants and Methods:330 healthy older adults between 65 and 89 years old (mean age = 71.6 ± 5.2) were recruited at the University of Florida (n = 222) and the University of Arizona (n = 108). Participants underwent resting-state fMRI and completed verbal memory (Hopkins Verbal Learning Test - Revised [HVLT-R]) and visuospatial memory (Brief Visuospatial Memory Test - Revised [BVMT-R]) measures. Immediate (total) and delayed recall scores on the HVLT-R and BVMT-R were calculated using each test manual’s scoring criteria. Learning ratios on the HVLT-R and BVMT-R were quantified by dividing the number of stimuli (verbal or visuospatial) learned between the first and third trials by the number of stimuli not recalled after the first learning trial. CONN Toolbox was used to extract average within-network connectivity values for CON, FPCN, and DMN. Hierarchical regressions were conducted, controlling for sex, race, ethnicity, years of education, number of invalid scans, and scanner site.
Results:Greater CON connectivity was significantly associated with better HVLT-R immediate (total) recall (ß = 0.16, p = 0.01), HVLT-R learning ratio (ß = 0.16, p = 0.01), BVMT-R immediate (total) recall (ß = 0.14, p = 0.02), and BVMT-R delayed recall performance (ß = 0.15, p = 0.01). Greater FPCN connectivity was associated with better BVMT-R learning ratio (ß = 0.13, p = 0.04). HVLT-R delayed recall performance was not associated with connectivity in any network, and DMN connectivity was not significantly related to any measure.
Conclusions:Connectivity within CON demonstrated a robust relationship with different components of memory function as well across verbal and visuospatial domains. In contrast, FPCN only evidenced a relationship with visuospatial learning, and DMN was not significantly associated with memory measures. These data suggest that CON may be a valuable target in longitudinal studies of age-related memory changes, but also a possible target in future non-invasive interventions to attenuate memory decline in older adults.
Validity of the NIH toolbox cognitive battery in a healthy oldest-old 85+ sample
- Sara A. Nolin, Hannah Cowart, Stacy Merritt, Katalina McInerney, P. K. Bharadwaj, Mary Kate Franchetti, David A. Raichlen, Cortney J. Jessup, G. Alex Hishaw, Emily J. Van Etten, Theodore P. Trouard, David S. Geldmacher, Virginia G. Wadley, Eric S. Porges, Adam J. Woods, Ron A. Cohen, Bonnie E. Levin, Tatjana Rundek, Gene E. Alexander, Kristina M. Visscher
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue 6 / July 2023
- Published online by Cambridge University Press:
- 14 October 2022, pp. 605-614
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Objective:
To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).
Method:Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.
Results:Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.
Conclusion:The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.
COMT in major depression - UK candidate gene association study
- A. Schosser, M.Y. Ng, A.W. Butler, S. Cohen-Woods, N. Craddock, M. Owen, I. Craig, A.E. Farmer, C.M. Lewis, P. McGuffin
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- European Psychiatry / Volume 26 / Issue S2 / March 2011
- Published online by Cambridge University Press:
- 16 April 2020, p. 813
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Catechol-O-methyltransferase (COMT) has a central role in brain dopamine, noradrenalin and adrenalin signaling, and has been suggested to be involved in the pathogenesis and pharmacological treatment of affective disorders. The functional single nucleotide polymorphism (SNP) in exon 4 (Val158Met, rs4680) influences the COMT enzyme activity. The Val158Met polymorphism is a commonly studied variant in psychiatric genetics, and initial studies in schizophrenia and bipolar disorder presented evidence for association with the Met allele. In unipolar depression, while some of the investigations point at an association between the Met/Met genotype and others have found a link between the Val/Val genotype and depression, most of the studies cannot detect any difference in Val158Met allele frequency between depressed individuals and controls.
In the present study, we further elucidated the impact of COMT polymorphisms including the Val158Met in MDD. We investigated 1,250 subjects with DSM-IV and/or ICD-10 diagnosis of major depression (MDD), and 1,589 control subjects from UK. A total of 24 SNPs spanning the COMT gene were successfully genotyped using the Illumina HumaHap610-Quad Beadchip (22 SNPs), SNPlex™ genotyping system (1 SNP), and Sequenom MassARRAY® iPLEX Gold (1 SNP). Statistical analyses were implemented using PASW Statistics18, FINETTI (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl), UNPHASED version 3.0.10 program and Haploview 4.0 program.
Neither single-marker nor haplotypic association was found with the functional Val158Met polymorphism or with any of the other SNPs genotyped. Our findings do not provide evidence that COMT plays a role in MDD or that this gene explains part of the genetic overlap with bipolar disorder.
P01-246-3Q29 case-control association study of co-morbid migraine in bipolar affective disorder
- M. Schmoeger, S. Cohen-Woods, G. Hosang, M. Schloegelhofer, I. Craig, A.E. Farmer, P. McGuffin, H.N. Aschauer, A. Schosser
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- European Psychiatry / Volume 26 / Issue S2 / March 2011
- Published online by Cambridge University Press:
- 16 April 2020, p. 247
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According to Oedegaard et al. (2010) the co-morbidity of migraine and bipolar disorder (BPD) is well documented in numerous epidemiological and clinical studies, and there are clear pathophysiological similarities. Interestingly, in a genome-wide scan, Lea et al. (2005) identified a susceptibility locus for a severe heritable form of common migraine on chromosome 3q29. With respect to BPD, a susceptibility region on chromosome 3q29 was identified in a genome-wide linkage scan (Bailer et al. 2002) and follow-up linkage analysis (Schosser et al. 2004). These findings were also supported by further fine-mapping of this region (Schosser et al. 2007). Since 3q29 is among the chromosomal regions implicated in migraine and bipolar linkage studies, the aim of the current study is to test for 3q29 association of migraine in sample of patients with BPD. The sample consists of 463 patients with a diagnosis of BPD (34.63% men, 65.37% women; mean age ± SD: 48.01 ± 11.26), as defined by the Diagnostic and Statistical Manual 4th edition operational criteria (DSM-IV) and the International Classification of Diseases 10th edition operational criteria (ICD-10), derived from the Bipolar Affective Disorder Case Control Study (BACCS). A total of 51 SNPs in the region of the 3q29 were genotyped using Sequenom MassARRAY® iPLEX Gold and tested for association with migraine. The results of this association study investigating the 3q29 region in a sample of patients with BPD will be presented.
Polygenic interactions with environmental adversity in the aetiology of major depressive disorder
- N. Mullins, R. A. Power, H. L. Fisher, K. B. Hanscombe, J. Euesden, R. Iniesta, D. F. Levinson, M. M. Weissman, J. B. Potash, J. Shi, R. Uher, S. Cohen-Woods, M. Rivera, L. Jones, I. Jones, N. Craddock, M. J. Owen, A. Korszun, I. W. Craig, A. E. Farmer, P. McGuffin, G. Breen, C. M. Lewis
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- Psychological Medicine / Volume 46 / Issue 4 / March 2016
- Published online by Cambridge University Press:
- 03 November 2015, pp. 759-770
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Background
Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD.
MethodThe RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them.
ResultsPRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10−6). SLEs and CT were also associated with MDD status (p = 2.19 × 10−4 and p = 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples.
ConclusionsCT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.
Contributors
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- By Tod C. Aeby, Melanie D. Altizer, Ronan A. Bakker, Meghann E. Batten, Anita K. Blanchard, Brian Bond, Megan A. Brady, Saweda A. Bright, Ellen L. Brock, Amy Brown, Ashley Carroll, Jori S. Carter, Frances Casey, Weldon Chafe, David Chelmow, Jessica M. Ciaburri, Stephen A. Cohen, Adrianne M. Colton, PonJola Coney, Jennifer A. Cross, Julie Zemaitis DeCesare, Layson L. Denney, Megan L. Evans, Nicole S. Fanning, Tanaz R. Ferzandi, Katie P. Friday, Nancy D. Gaba, Rajiv B. Gala, Andrew Galffy, Adrienne L. Gentry, Edward J. Gill, Philippe Girerd, Meredith Gray, Amy Hempel, Audra Jolyn Hill, Chris J. Hong, Kathryn A. Houston, Patricia S. Huguelet, Warner K. Huh, Jordan Hylton, Christine R. Isaacs, Alison F. Jacoby, Isaiah M. Johnson, Nicole W. Karjane, Emily E. Landers, Susan M. Lanni, Eduardo Lara-Torre, Lee A. Learman, Nikola Alexander Letham, Rachel K. Love, Richard Scott Lucidi, Elisabeth McGaw, Kimberly Woods McMorrow, Christopher A. Manipula, Kirk J. Matthews, Michelle Meglin, Megan Metcalf, Sarah H. Milton, Gaby Moawad, Christopher Morosky, Lindsay H. Morrell, Elizabeth L. Munter, Erin L. Murata, Amanda B. Murchison, Nguyet A. Nguyen, Nan G. O’Connell, Tony Ogburn, K. Nathan Parthasarathy, Thomas C. Peng, Ashley Peterson, Sarah Peterson, John G. Pierce, Amber Price, Heidi J. Purcell, Ronald M. Ramus, Nicole Calloway Rankins, Fidelma B. Rigby, Amanda H. Ritter, Barbara L. Robinson, Danielle Roncari, Lisa Rubinsak, Jennifer Salcedo, Mary T. Sale, Peter F. Schnatz, John W. Seeds, Kathryn Shaia, Karen Shelton, Megan M. Shine, Haller J. Smith, Roger P. Smith, Nancy A. Sokkary, Reni A. Soon, Aparna Sridhar, Lilja Stefansson, Laurie S. Swaim, Chemen M. Tate, Hong-Thao Thieu, Meredith S. Thomas, L. Chesney Thompson, Tiffany Tonismae, Angela M. Tran, Breanna Walker, Alan G. Waxman, C. Nathan Webb, Valerie L. Williams, Sarah B. Wilson, Elizabeth M. Yoselevsky, Amy E. Young
- Edited by David Chelmow, Virginia Commonwealth University, Christine R. Isaacs, Virginia Commonwealth University, Ashley Carroll, Virginia Commonwealth University
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- Book:
- Acute Care and Emergency Gynecology
- Published online:
- 05 November 2014
- Print publication:
- 30 October 2014, pp ix-xiv
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- By Frank Andrasik, Melissa R. Andrews, Ana Inés Ansaldo, Evangelos G. Antzoulatos, Lianhua Bai, Ellen Barrett, Linamara Battistella, Nicolas Bayle, Michael S. Beattie, Peter J. Beek, Serafin Beer, Heinrich Binder, Claire Bindschaedler, Sarah Blanton, Tasia Bobish, Michael L. Boninger, Joseph F. Bonner, Chadwick B. Boulay, Vanessa S. Boyce, Anna-Katharine Brem, Jacqueline C. Bresnahan, Floor E. Buma, Mary Bartlett Bunge, John H. Byrne, Jeffrey R. Capadona, Stefano F. Cappa, Diana D. Cardenas, Leeanne M. Carey, S. Thomas Carmichael, Glauco A. P. Caurin, Pablo Celnik, Kimberly M. Christian, Stephanie Clarke, Leonardo G. Cohen, Adriana B. Conforto, Rory A. Cooper, Rosemarie Cooper, Steven C. Cramer, Armin Curt, Mark D’Esposito, Matthew B. Dalva, Gavriel David, Brandon Delia, Wenbin Deng, Volker Dietz, Bruce H. Dobkin, Marco Domeniconi, Edith Durand, Tracey Vause Earland, Georg Ebersbach, Jonathan J. Evans, James W. Fawcett, Uri Feintuch, Toby A. Ferguson, Marie T. Filbin, Diasinou Fioravante, Itzhak Fischer, Agnes Floel, Herta Flor, Karim Fouad, Richard S. J. Frackowiak, Peter H. Gorman, Thomas W. Gould, Jean-Michel Gracies, Amparo Gutierrez, Kurt Haas, C.D. Hall, Hans-Peter Hartung, Zhigang He, Jordan Hecker, Susan J. Herdman, Seth Herman, Leigh R. Hochberg, Ahmet Höke, Fay B. Horak, Jared C. Horvath, Richard L. Huganir, Friedhelm C. Hummel, Beata Jarosiewicz, Frances E. Jensen, Michael Jöbges, Larry M. Jordan, Jon H. Kaas, Andres M. Kanner, Noomi Katz, Matthew S. Kayser, Annmarie Kelleher, Gerd Kempermann, Timothy E. Kennedy, Jürg Kesselring, Fary Khan, Rachel Kizony, Jeffery D. Kocsis, Boudewijn J. Kollen, Hubertus Köller, John W. Krakauer, Hermano I. Krebs, Gert Kwakkel, Bradley Lang, Catherine E. Lang, Helmar C. Lehmann, Angelo C. Lepore, Glenn S. Le Prell, Mindy F. Levin, Joel M. Levine, David A. Low, Marilyn MacKay-Lyons, Jeffrey D. Macklis, Margaret Mak, Francine Malouin, William C. Mann, Paul D. Marasco, Christopher J. Mathias, Laura McClure, Jan Mehrholz, Lorne M. Mendell, Robert H. Miller, Carol Milligan, Beth Mineo, Simon W. Moore, Jennifer Morgan, Charbel E-H. Moussa, Martin Munz, Randolph J. Nudo, Joseph J. Pancrazio, Theresa Pape, Alvaro Pascual-Leone, Kristin M. Pearson-Fuhrhop, P. Hunter Peckham, Tamara L. Pelleshi, Catherine Verrier Piersol, Thomas Platz, Marcus Pohl, Dejan B. Popović, Andrew M. Poulos, Maulik Purohit, Hui-Xin Qi, Debbie Rand, Mahendra S. Rao, Josef P. Rauschecker, Aimee Reiss, Carol L. Richards, Keith M. Robinson, Melvyn Roerdink, John C. Rosenbek, Serge Rossignol, Edward S. Ruthazer, Arash Sahraie, Krishnankutty Sathian, Marc H. Schieber, Brian J. Schmidt, Michael E. Selzer, Mijail D. Serruya, Himanshu Sharma, Michael Shifman, Jerry Silver, Thomas Sinkjær, George M. Smith, Young-Jin Son, Tim Spencer, John D. Steeves, Oswald Steward, Sheela Stuart, Austin J. Sumner, Chin Lik Tan, Robert W. Teasell, Gareth Thomas, Aiko K. Thompson, Richard F. Thompson, Wesley J. Thompson, Erika Timar, Ceri T. Trevethan, Christopher Trimby, Gary R. Turner, Mark H. Tuszynski, Erna A. van Niekerk, Ricardo Viana, Difei Wang, Anthony B. Ward, Nick S. Ward, Stephen G. Waxman, Patrice L. Weiss, Jörg Wissel, Steven L. Wolf, Jonathan R. Wolpaw, Sharon Wood-Dauphinee, Ross D. Zafonte, Binhai Zheng, Richard D. Zorowitz
- Edited by Michael Selzer, Stephanie Clarke, Leonardo Cohen, Gert Kwakkel, Robert Miller, Case Western Reserve University, Ohio
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- Book:
- Textbook of Neural Repair and Rehabilitation
- Published online:
- 05 May 2014
- Print publication:
- 24 April 2014, pp ix-xvi
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- By Frank Andrasik, Melissa R. Andrews, Ana Inés Ansaldo, Evangelos G. Antzoulatos, Lianhua Bai, Ellen Barrett, Linamara Battistella, Nicolas Bayle, Michael S. Beattie, Peter J. Beek, Serafin Beer, Heinrich Binder, Claire Bindschaedler, Sarah Blanton, Tasia Bobish, Michael L. Boninger, Joseph F. Bonner, Chadwick B. Boulay, Vanessa S. Boyce, Anna-Katharine Brem, Jacqueline C. Bresnahan, Floor E. Buma, Mary Bartlett Bunge, John H. Byrne, Jeffrey R. Capadona, Stefano F. Cappa, Diana D. Cardenas, Leeanne M. Carey, S. Thomas Carmichael, Glauco A. P. Caurin, Pablo Celnik, Kimberly M. Christian, Stephanie Clarke, Leonardo G. Cohen, Adriana B. Conforto, Rory A. Cooper, Rosemarie Cooper, Steven C. Cramer, Armin Curt, Mark D’Esposito, Matthew B. Dalva, Gavriel David, Brandon Delia, Wenbin Deng, Volker Dietz, Bruce H. Dobkin, Marco Domeniconi, Edith Durand, Tracey Vause Earland, Georg Ebersbach, Jonathan J. Evans, James W. Fawcett, Uri Feintuch, Toby A. Ferguson, Marie T. Filbin, Diasinou Fioravante, Itzhak Fischer, Agnes Floel, Herta Flor, Karim Fouad, Richard S. J. Frackowiak, Peter H. Gorman, Thomas W. Gould, Jean-Michel Gracies, Amparo Gutierrez, Kurt Haas, C.D. Hall, Hans-Peter Hartung, Zhigang He, Jordan Hecker, Susan J. Herdman, Seth Herman, Leigh R. Hochberg, Ahmet Höke, Fay B. Horak, Jared C. Horvath, Richard L. Huganir, Friedhelm C. Hummel, Beata Jarosiewicz, Frances E. Jensen, Michael Jöbges, Larry M. Jordan, Jon H. Kaas, Andres M. Kanner, Noomi Katz, Matthew S. Kayser, Annmarie Kelleher, Gerd Kempermann, Timothy E. Kennedy, Jürg Kesselring, Fary Khan, Rachel Kizony, Jeffery D. Kocsis, Boudewijn J. Kollen, Hubertus Köller, John W. Krakauer, Hermano I. Krebs, Gert Kwakkel, Bradley Lang, Catherine E. Lang, Helmar C. Lehmann, Angelo C. Lepore, Glenn S. Le Prell, Mindy F. Levin, Joel M. Levine, David A. Low, Marilyn MacKay-Lyons, Jeffrey D. Macklis, Margaret Mak, Francine Malouin, William C. Mann, Paul D. Marasco, Christopher J. Mathias, Laura McClure, Jan Mehrholz, Lorne M. Mendell, Robert H. Miller, Carol Milligan, Beth Mineo, Simon W. Moore, Jennifer Morgan, Charbel E-H. Moussa, Martin Munz, Randolph J. Nudo, Joseph J. Pancrazio, Theresa Pape, Alvaro Pascual-Leone, Kristin M. Pearson-Fuhrhop, P. Hunter Peckham, Tamara L. Pelleshi, Catherine Verrier Piersol, Thomas Platz, Marcus Pohl, Dejan B. Popović, Andrew M. Poulos, Maulik Purohit, Hui-Xin Qi, Debbie Rand, Mahendra S. Rao, Josef P. Rauschecker, Aimee Reiss, Carol L. Richards, Keith M. Robinson, Melvyn Roerdink, John C. Rosenbek, Serge Rossignol, Edward S. Ruthazer, Arash Sahraie, Krishnankutty Sathian, Marc H. Schieber, Brian J. Schmidt, Michael E. Selzer, Mijail D. Serruya, Himanshu Sharma, Michael Shifman, Jerry Silver, Thomas Sinkjær, George M. Smith, Young-Jin Son, Tim Spencer, John D. Steeves, Oswald Steward, Sheela Stuart, Austin J. Sumner, Chin Lik Tan, Robert W. Teasell, Gareth Thomas, Aiko K. Thompson, Richard F. Thompson, Wesley J. Thompson, Erika Timar, Ceri T. Trevethan, Christopher Trimby, Gary R. Turner, Mark H. Tuszynski, Erna A. van Niekerk, Ricardo Viana, Difei Wang, Anthony B. Ward, Nick S. Ward, Stephen G. Waxman, Patrice L. Weiss, Jörg Wissel, Steven L. Wolf, Jonathan R. Wolpaw, Sharon Wood-Dauphinee, Ross D. Zafonte, Binhai Zheng, Richard D. Zorowitz
- Edited by Michael E. Selzer, Stephanie Clarke, Leonardo G. Cohen, Gert Kwakkel, Robert H. Miller, Case Western Reserve University, Ohio
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- Textbook of Neural Repair and Rehabilitation
- Published online:
- 05 June 2014
- Print publication:
- 24 April 2014, pp ix-xvi
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- By Syed S. Ali, Nathan Allen, John E. Arbo, Elizabeth Arrington, Ani Aydin, Kenneth R. L. Bernard, Amy Caggiula, Nolan Caldwell, Jennifer L. Carey, Jennifer Carnell, Jayaram Chelluri, Michael N. Cocchi, Cristal Cristia, Vishal Demla, Bram Dolcourt, Andrew Eyre, Shawn Fagan, Brandy Ferguson, Sarah Fisher, Jonathan Friedstat, Brian C. Geyer, Brandon Godbout, Jeremy Gonda, Jeremy Goverman, Ashley L. Greiner, Casey Grover, Carla Haack, Abigail Hankin, John W. Hardin, Katrina L. Harper, Gregory Hayward, Stephen Hendriksen, Daniel Herbert-Cohen, Nadine Himelfarb, Calvin E. Hwang, Jacob D. Isserman, Joshua Jauregui, Joshua W. Joseph, Elena Kapilevich, Feras H. Khan, Sarvotham Kini, Karen A. Kinnaman, Ruth Lamm, Calvin Lee, Jarone Lee, Charles Lei, John Lemos, Daniel J. Lepp, Elisabeth Lessenich, Brandon Maughan, Julie Mayglothling, Kevin McConnell, Laura Medford-Davis, Kamal Medlej, Heather Meissen, Payal Modi, Joel Moll, Jolene H. Nakao, Matthew Nicholls, Lindsay Oelze, Carolyn Maher Overman, Viral Patel, Timothy C. Peck, Jeffrey Pepin, Candace Pettigrew, Byron Pitts, Zubaid Rafique, Chanu Rhee, Jonathan C. Roberts, Daniel Rolston, Steven C. Rougas, Benjamin Schnapp, Kathryn A. Seal, Raghu Seethala, Todd A. Seigel, Navdeep Sekhon, Kaushal Shah, Robert L. Sherwin, Kirill Shishlov, Ashley Shreves, Sebastian Siadecki, Jeffrey N. Siegelman, Liza Gonen Smith, Ted Stettner, Marie Carmelle Tabuteau, Joseph E. Tonna, N. Seth Trueger, Chad Van Ginkel, Bina Vasantharam, Graham Walker, Susan Wilcox, Sandra J. Williams, Matthew L. Wong, Nelson Wong, Samantha Wood, John Woodruff, Benjamin Zabar
- Edited by Kaushal Shah, Jarone Lee, Kamal Medlej, American University of Beirut, Scott D. Weingart
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- Practical Emergency Resuscitation and Critical Care
- Published online:
- 05 November 2013
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- 24 October 2013, pp xi-xx
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Estimating the heritability of reporting stressful life events captured by common genetic variants
- R. A. Power, T. Wingenbach, S. Cohen-Woods, R. Uher, M. Y. Ng, A. W. Butler, M. Ising, N. Craddock, M. J. Owen, A. Korszun, L. Jones, I. Jones, M. Gill, J. P. Rice, W. Maier, A. Zobel, O. Mors, A. Placentino, M. Rietschel, S. Lucae, F. Holsboer, E. B. Binder, R. Keers, F. Tozzi, P. Muglia, G. Breen, I. W. Craig, B. Müller-Myhsok, J. L. Kennedy, J. Strauss, J. B. Vincent, C. M. Lewis, A. E. Farmer, P. McGuffin
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- Journal:
- Psychological Medicine / Volume 43 / Issue 9 / September 2013
- Published online by Cambridge University Press:
- 14 December 2012, pp. 1965-1971
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Background
Although usually thought of as external environmental stressors, a significant heritable component has been reported for measures of stressful life events (SLEs) in twin studies.
MethodWe examined the variance in SLEs captured by common genetic variants from a genome-wide association study (GWAS) of 2578 individuals. Genome-wide complex trait analysis (GCTA) was used to estimate the phenotypic variance tagged by single nucleotide polymorphisms (SNPs). We also performed a GWAS on the number of SLEs, and looked at correlations between siblings.
ResultsA significant proportion of variance in SLEs was captured by SNPs (30%, p = 0.04). When events were divided into those considered to be dependent or independent, an equal amount of variance was explained for both. This ‘heritability’ was in part confounded by personality measures of neuroticism and psychoticism. A GWAS for the total number of SLEs revealed one SNP that reached genome-wide significance (p = 4 × 10−8), although this association was not replicated in separate samples. Using available sibling data for 744 individuals, we also found a significant positive correlation of R2 = 0.08 in SLEs (p = 0.03).
ConclusionsThese results provide independent validation from molecular data for the heritability of reporting environmental measures, and show that this heritability is in part due to both common variants and the confounding effect of personality.
The current state of play on the molecular genetics of depression
- S. Cohen-Woods, I. W. Craig, P. McGuffin
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- Journal:
- Psychological Medicine / Volume 43 / Issue 4 / April 2013
- Published online by Cambridge University Press:
- 12 June 2012, pp. 673-687
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Background
It has been well established that both genes and non-shared environment contribute substantially to the underlying aetiology of major depressive disorder (MDD). A comprehensive overview of genetic research in MDD is presented.
MethodPapers were retrieved from PubMed up to December 2011, using many keywords including: depression, major depressive disorder, genetics, rare variants, gene–environment, whole genome, epigenetics, and specific candidate genes and variants. These were combined in a variety of permutations.
ResultsLinkage studies have yielded some promising chromosomal regions in MDD. However, there is a continued lack of consistency in association studies, in both candidate gene and genome-wide association studies (GWAS). Numerous factors may account for variable results including the use of different diagnostic approaches, small samples in early studies, population stratification, epigenetic phenomena, copy number variation (CNV), rare variation, and phenotypic and allelic heterogeneity. The conflicting results are also probably, in part, a consequence of environmental factors not being considered or controlled for.
ConclusionsEach research group has to identify what issues their sample may best address. We suggest that, where possible, more emphasis should be placed on the environment in molecular behavioural genetics to identify individuals at environmental high risk in addition to genetic high risk. Sequencing should be used to identify rare and alternative variation that may act as a risk factor, and a systems biology approach including gene–gene interactions and pathway analyses would be advantageous. GWAS may require even larger samples with reliably defined (sub)phenotypes.