Prefrontal cortical volume loss is associated with stress-related deficits in verbal learning and memory in HIV-infected women

Abstract RTOG 3508/AbbVie M13-813/INTELLANCE-1 was a phase 3 trial of depatuximab-mafodotin (depatux-m, formerly ABT-414) that accrued 639 patients with EGFR-amplified newly diagnosed GBM. At the pre-specified interim OS analysis, the futility criteria were met and there was no survival benefit from adding depatux-m to SOC. Pre-specified secondary NCF analyses included time to decline in verbal learning and memory as assessed by the HVLT-R Total Recall based on the reliable change index. Exploratory NCF analyses examined changes in other HVLT-R outcomes over time. As corneal epitheliopathy causing visual impairment is a known toxicity of depatux-m, NCF tests that did not depend on visual acuity were employed. NCF testing occurred at baseline, day 1 of the first cycle of adjuvant depatux-m, every other cycle (i.e., 8 weeks) thereafter, and at progression. Compliance with test completion was 95% at screening and 80%, 70%, 58%, 51%, 47% thereafter through cycle 9. The most common reasons for missing data was site error. Time to HVLT-R Total Recall decline trended worse in the depatux-m arm compared to placebo but the difference was not significant (12 month deterioration: 41.2%, 95% CI: 3.50–47.2 vs 32.4%, 95% CI: 26.6- 38.4, p=0.052). The depatux-m arm, in comparison to the placebo arm, showed greater decline from baseline on the HVLT-R at the following time points: cycle 3 (Total Recall: mean= -1.8, SD=5.7 vs mean= -0.5, SD=5.5, respectively, p=0.046; Delayed Recall: mean= -1.1, SD=3.0 vs. mean= -0.2, SD=2.7, respectively, p=0.01), cycle 7 (Total Recall: mean= -0.6, SD=5.1 vs mean= 1.4, SD=5.0, respectively, p=0.009; Delayed Recall: mean -0.6, SD=3.0 vs. mean= 0.5, SD=2.7, respectively, p=0.01), and cycle 9 (Delayed Recall: mean=-0.4, SD=2.7 vs. mean= 0.8, SD=2.4, respectively, p=0.003). Depatux-m added to concurrent chemoradiation and adjuvant temozolomide was associated with faster time to deterioration and worse episodic learning and memory over time than placebo.

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Verbal learning and memory in neuropsychologically defined subgroups of Parkinson's disease patients

Archives of Clinical Neuropsychology ◽

10.1093/arclin/11.5.422 ◽

1996 ◽

Vol 11 (5) ◽

pp. 422-422

Author(s):

L. Manade ◽

A.I. Troster ◽

J.A. Fields ◽

A.M. Paolo ◽

W.C. Koller

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Learning And Memory ◽

Verbal Learning

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Effect of environmental manganese exposure on verbal learning and memory in Mexican children

Environmental Research ◽

10.1016/j.envres.2012.10.007 ◽

2013 ◽

Vol 121 ◽

pp. 39-44 ◽

Cited By ~ 62

Author(s):

R. Torres-Agustín ◽

Y. Rodríguez-Agudelo ◽

A. Schilmann ◽

R. Solís-Vivanco ◽

S. Montes ◽

...

Keyword(s):

Learning And Memory ◽

Verbal Learning ◽

Manganese Exposure ◽

Mexican Children

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Relationship of Depression and function of Attention, Planning and Verbal Learning and Memory

Thematics Journal of Geography ◽

10.26643/tjg.v8i9.8420 ◽

2019 ◽

Vol 8 (9) ◽

pp. 214-225

Author(s):

Nongmeikapam Premika Devi

Keyword(s):

Learning And Memory ◽

Verbal Learning ◽

Education Level ◽

Auditory Verbal Learning ◽

And Function ◽

Age Range ◽

Relationship Of ◽

The Relationship ◽

Minimum Education ◽

Hiv Aids

The present study examines the relationship of depression and the neuropsychologicalfunction of attention, planning and auditory verbal learning and memory among individualswith HIV/AIDS. 200 subjects who were HIV/AIDS positive (100 males and 100 females) andwere within age range of 20 to 50 years and minimum education level of 8th standard weretaken. The result indicates that Depression slows down the performance of attention; alsodepression most likely decreases the function of auditory verbal learning and memory

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Surface-based analysis of cortical thickness and volume loss in Alzheimer’s disease

Proceedings of IMPRS ◽

10.18060/23524 ◽

2019 ◽

Vol 2 (1) ◽

Author(s):

Emily Iannopollo ◽

Ryan Plunkett ◽

Kara Garcia

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Cortical Thickness ◽

Surface Matching ◽

Cortical Surface ◽

Volume Loss ◽

Cortical Volume ◽

Human Connectome Project ◽

And Control

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness. Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9. Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss. Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness. References 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65. 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194. 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207. 4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355. 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84. 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24. 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4. 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24. 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18.

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