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Factors associated with the Big 5 personality traits in Alzheimer’s disease and related dementias (ADRD)

Alzheimer's & Dementia 2022年第S7期18卷

作者： Emma Rhodes Lauren Massimo Dawn Mechanic-Hamilton Corey T McMillan Jeffrey S Phillips Sarah E Burke Quinn Hlava Philip Cook James C. Gee Murray Grossman University of Pennsylvania Frontotemporal Degeneration Center Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA University of Pennsylvania Frontotemporal Degeneration Center PHILADELPHIA PA USA Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Philadelphia PA USA

Background Informant ratings of behavior and personality change are the gold standard of measurement in Alzheimer’s disease and related dementias (ADRD) where insight is often impaired, but informant report can be bi...

Background Informant ratings of behavior and personality change are the gold standard of measurement in Alzheimer’s disease and related dementias (ADRD) where insight is often impaired, but informant report can be biased by psychosocial factors such as caregiver burden. This study assessed the impact of caregiver burden on informant ratings of patient Big 5 personality traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness) and associations between degree of discrepancy between informant and patient ratings with cortical volume in regions implicated in loss of insight. Method A sample of 70 ADRD participants with heterogeneous neurodegenerative clinical phenotypes (Figure 1) and their informants completed the NEO Personality Inventory (NEO-PI). Disease severity was measured using the Clinical Dementia Rating Scale (CDR). Caregiver burden was measured using the Zarit Burden Interview (ZBI). Discrepancy scores were computed for all NEO-PI Big 5 scores and a global discrepancy score. Participants were classified as having high/low discrepancy using a median split for each NEO-PI discrepancy score. T1-weighted 3T MRI grey matter volumes were normalized to intracranial volume for all ROIs in the Schaefer100×17 parcellation, and linear regression analyses related global difference score to Bonferroni-corrected atrophy regions. Result Higher levels of caregiver burden were associated with higher informant ratings of patient Neuroticism (ß = 0.02, p = .013) and lower informant ratings of patient Agreeableness (ß = -0.02, p = .0004), Conscientiousness (ß = -0.02, p = .005), and Openness (ß = -0.01, p = .014) independent of disease severity. Patients with high global discrepancy scores showed reduced cortical volumes in left ventral PFC, right medial PFC, and right superior temporal gyrus. Patients with low discrepancy scores showed relatively preserved cortical volumes in bilateral somatomotor cortex, right posterior cingulate, and left supplementary m

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Deep Learning Registration with Scan-temporal Consistency May Improve Detection of Alzheimer’s Disease Progression

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Alzheimer's & Dementia 2022年第S5期18卷

作者： Mengjin Dong Long Xie Jiancong Wang Sandhitsu R. Das David A. Wolk Paul A. Yushkevich Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center Perelman School of Medicine Philadelphia PA USA

Background Structural changes in Alzheimer’s disease (AD) can be detected as atrophy in the medial temporal lobe (MTL), which is normally studied by applying volumetric or deformation-based morphometry (DBM) to longi...

Background Structural changes in Alzheimer’s disease (AD) can be detected as atrophy in the medial temporal lobe (MTL), which is normally studied by applying volumetric or deformation-based morphometry (DBM) to longitudinal scans. However, conventional DMB methods are computationally intensive and may not estimate the atrophy accurately due to the noise in the longitudinal images, e.g., head motion or MRI artifacts. We hypothesize that deep learning models can be trained to estimate atrophy from longitudinal MRI scans and are more sensitive to biological changes rather than artifactual. Method Two models were trained on longitudinal T1 MRI from 155 ADNI participants and tested on 326 participants. First, a deep learning-based registration model Voxelmorph (Balakrishnan et al., 2019) was applied to generate a deformation field to calculate MTL atrophy rate for each pair of images. Hypothesizing that noise is independent of time while neurodegeneration is time dependent, we then extended Voxelmorph to incorporate a loss that encourages the model to correctly infer the input scan temporal order (STO). In addition, in order to preserve the deformable registration nature of Voxelmorph, it’s weights were held fixed during network training, and instead a U-Net reconstruction network was introduced, to synthesize inputs to Voxelmorph. Models were compared with a DBM method ALOHA (Das et al., 2012). Result Voxelmorph generated atrophy measurements comparable to ALOHA in terms of differentiating AD stages (Figure 2) and STO consistency (Table 2). Voxelmorph + STO model has increased STO consistency and can more significantly differentiate disease stages (in absolute terms), especially between the normal controls and preclinical AD. Conclusion Deep learning-based registration method Voxelmorph achieves comparable performance in longitudinal analysis of subtle changes as in DBM method ALOHA. With the introduction of a scan temporal order loss and reconstruction network, the mod

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Tau accumulation and degeneration differ across functional networks in atypical Alzheimer's disease phenotypes

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Alzheimer's & Dementia 2022年第S6期18卷

作者： Jeffrey S Phillips Sarah E Burke Katheryn A Q Cousins Sanaz Arezoumandan Daniel T Ohm Min Chen Philip Cook Jacob G. Dubroff Ilya M. Nasrallah Corey T McMillan Murray Grossman David A. Wolk David J. Irwin Penn FTD Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn FTD Center Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Penn FTD Center University of Pennsylvania Philadelphia PA USA Digital Neuropathology Laboratory Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA

Background Recent models of Alzheimer's disease (AD) posit that pathogenic tau propagates through interconnected structural networks, suggesting amnestic and non-amnestic AD (aAD & naAD) phenotypes could refle...

Background Recent models of Alzheimer's disease (AD) posit that pathogenic tau propagates through interconnected structural networks, suggesting amnestic and non-amnestic AD (aAD & naAD) phenotypes could reflect differential involvement of functional networks. We tested this hypothesis by contrasting longitudinal grey matter (GM) atrophy and tau accumulation across AD clinical phenotypes in 7 canonical functional networks. Methods We analyzed longitudinal 3-Tesla T1-weighted MRI from 122 patients [aAD=49; logopenic primary progressive aphasia (lvPPA)=31; posterior cortical atrophy (PCA)=23; behavioral/dysexecutive AD (bvAD)=6; corticobasal syndrome (CBS)=6; mixed naAD=4] with autopsy or CSF evidence of AD pathology and 91 cognitively normal controls (Table 1). Thirty-one patients (aAD=9; lvPPA=7; PCA=9; bvAD=2; CBS=3; naAD=1) had longitudinal flortaucipir PET (FTP-PET) data. We computed baseline values and annualized change for GM volume and FTP-PET standardized uptake value ratios (SUVRs) in the 100-label, 7-network parcellation of Schaefer et al. (2018). Linear mixed effects models (α=0.05, corrected) adjusting for MMSE assessed group differences in atrophy and tau accumulation. Results All groups exhibited significant baseline and longitudinal atrophy vs. controls in multiple networks (Figs. 1-2). Differences in longitudinal atrophy rate (Fig. 2) were observed in the dorsal attention network [PCA>lvPPA, t(371)=3.1, p<0.04], salience network [PCA>aAD, t(371)=3.1, p<0.04], and limbic network [CBS>aAD, t(371)=3.3, p<0.02; CBS>bvAD, t(371)=3.7, p<0.006]. PCA patients exhibited FTP-PET SUVR increases in all networks (all p<0.02), while aAD patients displayed increases in all networks except visual and limbic (all others p<0.0001). LvPPA patients exhibited net SUVR increases only in the somatomotor network [t(97)=3.5, p<0.001]. At baseline, lvPPA patients had left > right atrophy in all networks [all t>3.7, p<0.0002]; right > left atrophy was observed in the visual net

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PART is part of SNAP-MCI

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Alzheimer's & Dementia 2022年第S6期18卷

作者： Laura EM Wisse Long Xie Xueying Lyu Sandhitsu R. Das Robin de Flores Jacqueline Lane Paul A. Yushkevich David A. Wolk Disease Neuroimaging Initiative Lund University Lund Sweden University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA

Background Mild Cognitive Impairment with Suspected non-Alzheimer’s Pathophysiology (SNAP-MCI) represents a group of patients with Alzheimer’s disease (AD) like neurodegeneration without beta-amyloid pathology. Whil...

Background Mild Cognitive Impairment with Suspected non-Alzheimer’s Pathophysiology (SNAP-MCI) represents a group of patients with Alzheimer’s disease (AD) like neurodegeneration without beta-amyloid pathology. While this group likely has a heterogeneous etiology, tau pathology, as in Primary Age-Related Tauopathy (PART), has been hypothesized to play a major role. We investigate tau positron emission tomography (PET) uptake in the medial temporal lobe (MTL) in SNAP-MCI and the association of MTL Tau-PET uptake with structural measures and delayed recall in beta-amyloid negative (A-) MCI patients. Method 237 MCI patients and 301 A- controls with available beta-amyloid and tau PET and magnetic resonance images (within 200 days) were included. Baseline hippocampal volume and entorhinal cortex (ERC) and Brodmann areas (BA)35 thickness were obtained using an in-house developed pipeline and annualized atrophy rates were estimated in an unbiased fashion using follow-up MRIs within 4.5 years. Βeta-amyloid status (A+/-) was determined by a standard cut-off (Florbetapir: 1.11; Florbetaben: 1.08) and neurodegeneration status (N+/-) by hippocampal volumes, corrected for intracranial volume, using the 90 th percentile of A+ AD patients as the cut-off. Tau-PET standardized uptake value ratio (SUVR) in the ERC/BA35 was calculated. A composite z-score of delayed recall at baseline and change over 2 years was obtained. Result SNAP-MCI had significantly higher ERC/BA35 Tau-PET SUVR than A- controls (Table 1, corrected for age) and qualitatively higher than A-N- MCI (p=0.10). ERC/BA35 Tau-PET SUVR in A-N- MCI was not significantly different from A- controls. In A- MCI patients, ERC/BA35 Tau-PET SUVR was significantly associated with BA35 thickness and hippocampal, ERC and BA35 atrophy rates, corrected for beta-amyloid PET SUVR (Table 2; Figure 1). MTL structural measures, but not ERC/BA35 Tau-PET SUVR, were associated with cross-sectional and longitudinal delayed recall measures (Tab

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Detecting biological changes in longitudinal MRI scans of Alzheimer’s disease patients in the hippocampus region with deep learning

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Alzheimer's & Dementia 2020年第S5期16卷

作者： Mengjin Dong Long Xie Sandhitsu R. Das David A. Wolk Paul A. Yushkevich Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn Memory Center University of Pennsylvania Philadelphia PA USA

Background Alzheimer's Disease (AD) is characterized by gradual loss of gray matter, particularly in the medial temporal lobe (MTL) and hippocampus. Longitudinal change in hippocampal volume on MRI is an effective...

Background Alzheimer's Disease (AD) is characterized by gradual loss of gray matter, particularly in the medial temporal lobe (MTL) and hippocampus. Longitudinal change in hippocampal volume on MRI is an effective biomarker to monitor disease progression in AD. However, neurodegenerative changes in the hippocampus on longitudinal MRI can be obscured by differences in MRI signal that are unrelated to disease progression, such as different amounts of head motion, noise, or MRI artifact. We hypothesize that a deep learning model can be trained to distinguish true biological changes from nonsystematic factors, leading to a more sensitive biomarker of disease progression. Method A 3D convolutional neural network (CNN) was trained to predict the inter-scan interval (in days) from pairs of rigidly co-registered longitudinal T1-weighted MRI scans. Scans trimmed to the hippocampus region were input in arbitrary time order, and the network had to infer which was the earlier scan and the interscan interval in days. A cohort of 666 ADNI participants was used for CNN training and 154 participants formed a held-out test set. We tested the hypotheses that (a) CNN can correctly predict the temporal order of scans (indicating sensitivity to biological changes) and (b) that a measure of mismatch between actual interscan interval and interval predicted by a CNN trained on more impaired subjects (late MCI and AD) would differ between less impaired (controls + early MCI) and more impaired groups (indicating sensitivity to AD). The same hypotheses were also tested using a conventional technique for longitudinal change estimation, ALOHA (Das et al., 2012). Result CNN was more accurate than ALOHA in inferring the temporal order of scans (Table 2, Figure 1) and to a lesser extent, inter-scan interval (Table 3, Figure 2). Mismatch measure was greater for the less impaired than more impaired group for both CNN and ALOHA, with larger difference for CNN, potentially indicating greater sensitivi

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Neurodegeneration in medial temporal lobe subregions propagates within distinct functional networks in the AD continuum

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Alzheimer's & Dementia 2020年第S5期16卷

作者： Robin de Flores Sandhitsu R. Das Long Xie Laura Wisse Preya Shah Paul A. Yushkevich David A. Wolk University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA

Background The medial temporal lobe (MTL) is extensively connected to the rest of the brain through two specific networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. As neurodegeneration is thou...

Background The medial temporal lobe (MTL) is extensively connected to the rest of the brain through two specific networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. As neurodegeneration is thought to occur along specific networks, our aim was to evaluate whether atrophy in perirhinal (PRC) vs parahippocampal cortices (PHC) - two MTL nodes of the AT vs PM systems, respectively - covary over time with distinct brain regions reflecting the AT vs PM networks. Method Longitudinal T1-weighted images (up to 3 scans, span: 1.73±0.49 years) of 117 Ab- Cognitively Normal elderly (CN), 65 Ab+ CN, 148 Ab+ MCI and 19 Ab+ AD individuals from ADNI were included. Annualized atrophy rates of PRC and PHC were estimated with an unbiased deformation-based morphometry method. Whole-brain cortical thickness annualized atrophy rates were estimated using ANTs. Voxel-wise correlations between MTL measures and whole-brain maps were performed to highlight structural covariance patterns [P cluster(corr) <0.05, P voxel(uncorr) <0.01]. Functional connectivity analyses (seed-based) were employed to define the AT and PM networks [P cluster(corr) <0.05, P voxel(uncorr) <0.01] in an independent sample of 68 CN older adults. The overlap between structural covariance patterns and the AT and PM networks was visually inspected. T-maps from the voxel-wise correlations were used to calculate goodness of fit (GOF) indices with the AT and PM networks (GOF=t inside -t outside ). Result Functional connectivity analyses revealed two distinct networks: the AT network encompasses the amygdala and the temporopolar cortex extending posteriorly to inferior and middle temporal gyri while the PM network encompasses the posterior cingulate cortex, the precuneus, the cuneus, the angular and occipital gyri and the right insula (Figure 1-a). PRC structurally covaried with the amygdala and the left temporopolar cortex, overlapping with the AT network (Figure 1-b,c). In contrast, PHC covaried with t

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Reduced longitudinal change in18F-flortaucipir PET is associated with clinical phenotype in atypical Alzheimer’s disease

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Jeffrey S Phillips Frederick J. Nitchie IV Fulvio Da Re Christopher A Olm Philip Cook Corey T McMillan David J. Irwin James C. Gee Jacob G. Dubroff Murray Grossman Ilya M. Nasrallah Penn FTD Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA School of Medicine and Surgery Milan Center for Neuroscience (NeuroMI) University of Milano-Bicocca Milan Italy Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Digital Neuropathology Laboratory Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Longitudinal positron emission tomography (PET) studies of tau accumulation in Alzheimer’s disease (AD) have noted reduced increases or frank decreases in tau signal. It is unclear whether these reductions...

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Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early AD

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Long Xie Sandhitsu R. Das Laura Wisse Ranjit Ittyerah Robin de Flores Paul A. Yushkevich David A. Wolk Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Inserm Inserm UMR-S U1237 Université de Caen-Normandie GIP Cyceron Caen France Penn Memory Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background In clinical trials targeting early Alzheimer’s disease (AD), recruiting participants that are more likely to progress over the course of the trials is crucial to success. Molecular and neurodegenerative ma...

Background In clinical trials targeting early Alzheimer’s disease (AD), recruiting participants that are more likely to progress over the course of the trials is crucial to success. Molecular and neurodegenerative markers obtained through PET or in cerebrospinal fluid have significant prognostic value, but they are costly or invasive. In this study, we hypothesize that a combination of plasma and structural MRI biomarkers, which overcome these limitations, are predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a more feasible solution. Methods Longitudinal 3 Tesla T1-weighted MRI and cognitive measurements [logical memory, delayed recall and clinical dementia rating sum-of-boxes] of 662 cognitively normal (CN) and MCI participants [divided into β-amyloid positive/negative (A+/A-)] from ADNI were included (Table 1). Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI. Annualized change rates of BA35 thickness [the first region of neurofibrillary tangle pathology] and cognitive measurements were estimated using linear modeling. In each diagnostic group and A+/A- subgroups, stepwise linear regression model with age, gender, education and intracranial volume as fixed variables and all baseline MRI and plasma measurements [neurofilament light chain (NfL), pTau-181] was implemented to investigate their predictive power for each longitudinal measure. In addition, the area under the curve (AUC) of each model in discriminating the first and last terciles of each longitudinal measurement was reported. Results Baseline plasma and structural biomarkers provided complementary information in predicting longitudinal atrophy and cognitive decline in controls and MCI (Table 2, Figure 1). These relationships were maintained (albeit with some differences in most predicted measures) when limited to A+ subgroups (preclinical and prodromal AD, Table

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A multi-site study of Alzheimer’s disease neuroimaging and cognitive biomarkers in 2819 cognitively normal individuals

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Alzheimer's & Dementia 2023年第S14期19卷

作者： Long Xie Sandhitsu R. Das Laura EM Wisse Xueying Lyu Emily McGrew Robin de Flores Eli Gibson Paul A. Yushkevich David A. Wolk Alzheimer’s Disease Neuroimaging Initiative Siemens Healthineers Princeton NJ USA Correspondece Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Department of Diagnostic Radiology Clinical Sciences Lund University Lund Sweden University of Pennsylvania Philadelphia PA USA Normandie Univ UNICAEN INSERM U1237 PhIND “Physiopathology and Imaging of Neurological Disorders” NeuroPresage Team GIP Cyceron Caen France Siemens Heathineers Princeton NJ USA Institute on Aging University of Pennsylvania Philadelphia PA USA

Background Developing effective Alzheimer’s disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinic...

Background Developing effective Alzheimer’s disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinical AD. However, the generalizability of these biomarkers in large, multi-site cohorts and their power to predict disease progression remain unclear. Structural MRI, tau PET and standardized cognitive tests of 2819 CN adults were pooled from three studies to investigate the power of these measures in discriminating β-amyloid positivity (A+/-) and predicting disease progression. Methods T1-weighted MRI and cognitive data of 2819 CN (A-/A+: 1180/1540, Table 2) individuals from ADNI, HABS and A4 were included. Baseline medial temporal lobe structural measures were extracted from MRI (complete list in Table 1). For participants with prospective longitudinal MRI or cognitive measures (4.5 years), annualized change rate of each measurement was estimated. ANCOVA and receiver operating characteristic analyses were used to test biomarker differences between A+/A-. In 563 CN with cross-sectional tau PET available, stepwise linear mixed effect modeling was performed to identify the subset of baseline cross-sectional biomarkers (tau, MRI and cognition) that yields the optimal model (smallest Akaike information criterion) in predicting longitudinal atrophy and cognitive decline. The area under the curve (AUC) of each model in discriminating the first (fast) and last (slow progressors) tertiles of each longitudinal measurement was reported. Results Significant differences between A+/A- (Table 2) were observed in cross-sectional tau (most significant), cross-sectional and longitudinal cognitive, as well as longitudinal structural MRI measures. When predicting disease progression with baseline measures (Figure 1-a), tau (selected in all the models) and cognitive biomarkers were included in the most predictive models while MRI biomarkers did not provide additional informa

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Development of the mobile cognitive app performance platform for detection of AD-related cognitive change

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Alzheimer's & Dementia 2020年第S11期16卷

作者： Dawn Mechanic-Hamilton Sean Lydon Kimberly Halberstadter Jacqueline Lane Sandhitsu R. Das David A. Wolk Penn Memory Center University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA

Background This pilot study investigates the psychometric properties of a mobile app-based memory task, the mobile cognitive app performance platform (mCAPP), to detect memory changes associated with preclinical Alzhe...

Background This pilot study investigates the psychometric properties of a mobile app-based memory task, the mobile cognitive app performance platform (mCAPP), to detect memory changes associated with preclinical Alzheimer’s Disease (AD). A mobile, reliable, valid, short, and engaging cognitive assessment is necessary for early detection and tracking change in individuals at risk for AD. Method Participants included 30 older adults (60% female; age=70.9±4.1, years of education=16±2.5; 73% Caucasian) with normal cognition who are enrolled in the penn ADRC cohort. The mCAPP memory task includes learning and then matching hidden card pairs and incorporates increasing memory load, pattern separation features (lure vs. non-lure), and spatial memory (moving target cards). Participants completed the NACC UDS3 neuropsychological battery and additional cognitive measures, within six months of the mCAPP testing and twenty participants completed a high-resolution structural MRI scan. Result Almost all participants found the difficulty level of the mCAPP game to be “just right” (92%), 65% reported they enjoyed the game and 43% reported they would play it for fun. Higher average number of guesses at higher memory load levels was correlated with lower story learning and memory, digit span, trail making test (TMT) B and olfactory performance (p<0.05). Higher average duration of time on the higher memory load levels was correlated with worse performance on digit span, animal naming, TMT A and B, and olfaction (p<0.05). The number of guesses and duration of time on lure vs. non-lure trials was significantly different, with more guesses and more time required for the lure trials (requires pattern separation) at higher memory load levels [Guesses: 8 card, t(29)=5.6,p<0.001; 12 cards, t(29)=4.5,p<0.001; Duration: 8 cards, t(29)=6.9,p<0.001; 12 cards, t(29)=5.9,p<0.001]. At some higher memory load levels, longer duration of time on trials correlated with smaller mean thickness of the par

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