Yaakov Stern

YNIMG Journal 2025 Journal Article

Dynamic brain states during reasoning tasks: a co-activation pattern analysis

• Fatemeh Hasanzadeh
• Christian Habeck
• Yaakov Stern

Brain activity exhibits substantial temporal variability during cognitive processes, yet traditional fMRI analyses often fail to capture these dynamic patterns. Co-activation pattern (CAP) analysis has emerged as a promising method to study brain dynamics. CAP analysis provides a powerful framework for capturing transient brain states, however, its application to cognitive tasks remains very limited, with no prior studies specifically investigating its role in reasoning performance. This study investigated CAPs during reasoning tasks, their relationship with cognitive performance, age and other individual differences. We applied CAP analysis to fMRI data from 303 participants performing three reasoning tasks-Matrix Reasoning, Letter Sets, and Paper Folding-along with resting-state data. Using K-means clustering, we identified four distinct CAPs, each exhibiting unique spatial and temporal characteristics. These CAPs were analyzed in relation to predefined resting-state networks, revealing their functional relevance to cognitive task engagement. Key temporal metrics, including fraction occupancy, dwelling time, and transition probabilities, were assessed across reasoning tasks and resting state. The results demonstrate that CAP2 and CAP3 are predominantly engaged during reasoning tasks, with CAP2 strongly overlapping with the visual network and CAP3 exhibiting concurrent default mode and sensorimotor network activations. CAP1, primarily dominant during rest, showed prolonged engagement in older individuals, while CAP4 appeared to function as a transitional state facilitating network reorganization. Regression analyses link longer dwelling times and higher fraction occupancy of CAP2 and CAP3 to superior reasoning performance, whereas excessive transitions to CAP4 negatively impacted cognitive task outcomes. Additionally, aging was associated with reduced engagement in task-relevant CAPs and an increased tendency to transition into baseline-like states. These findings underscore the critical role of dynamic brain state reconfigurations in supporting cognition specifically reasoning and highlight CAP analysis as a powerful tool for studying transient brain function and individual cognitive differences.

YNICL Journal 2023 Journal Article

Distinct and joint effects of low and high levels of Aβ and tau deposition on cortical thickness

• Seyed Hani Hojjati
• Tracy A. Butler
• Gloria C. Chiang
• Christian Habeck
• Arindam RoyChoudhury
• Farnia Feiz
• Jacob Shteingart
• Siddharth Nayak

F-Florbetapir positron emission tomography (PET) and magnetic resonance imaging (MRI) to quantify tau, Aβ, and cortical thickness in 590 participants ranging in age from 20 to 90. We performed multiple regression analyses to assess the distinct and joint effects of Aβ and tau on cortical thickness using 590 healthy control (HC) and mild cognitive impairment (MCI) participants (141 young, 394 HC elderlies, 52 MCI). We showed thatin participants with normal levels of global Aβdeposition, Aβ uptakewassignificantly associated with increasedcortical thickness regardless of tau (e.g., left entorhinal cortex with t > 3.241, p < 0.0013). The relationship between tau deposition and neurodegeneration was more complex: in participants with abnormal levels of global tau, tau uptake was associated with cortical thinning in several regions of the brain (e.g., left entorhinal with t < -2.80, p < 0.0096 and left insula with t-value < -4.284, p < 0.0001), as reported on prior neuroimaging and neuropathological studies. Surprisingly, in participants with normal levels of global tau, tau was found to be associated with cortical thickening. Moreover, in participants with abnormal levels of global Aβandtau, theresonancebetween them, defined as their correlation throughout the cortex, wasassociated strongly with cortical thinning even when controlling for a direct linear effect. We confirm prior findings of an association between Aβ deposition and cortical thickening and suggest this may also be the case in the earliest stages of deposition in normal aging. We also illustrate that resonance between high levels of Aβ and tau uptake is strongly associated with cortical thinning, emphasizing the effects of Aβ/tau synergy inAD pathogenesis.

YNIMG Journal 2023 Journal Article

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort

• Georgette Argiris
• Yaakov Stern
• Seonjoo Lee
• Hyunnam Ryu
• Christian Habeck

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al. , 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e. g. , Gracia-Tabuenca et al. , 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20–80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i. e. , Stern et al. , 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

YNIMG Journal 2022 Journal Article

The role of neural flexibility in cognitive aging

• Eleanna Varangis
• Weiwei Qi
• Yaakov Stern
• Seonjoo Lee

Studies assessing relationships between neural and cognitive changes in healthy aging have shown that a variety of aspects of brain structure and function explain a significant portion of the variability in cognitive outcomes throughout adulthood. Many studies assessing relationships between brain function and cognition have utilized time-averaged, or static functional connectivity methods to explore ways in which brain network organization may contribute to aspects of cognitive aging. However, recent studies in this field have suggested that time-varying, or dynamic measures of functional connectivity, which assess changes in functional connectivity over the course of a scan session, may play a stronger role in explaining cognitive outcomes in healthy young adults. Further, both static and dynamic functional connectivity studies suggest that there may be differences in patterns of brain-cognition relationships as a function of whether or not the participant is performing a task during the scan. Thus, the goals of the present study were threefold: (1) assess whether neural flexibility during both resting as well as task-based scans is related to participant age and cognitive performance in a lifespan aging sample, (2) determine whether neural flexibility moderates relationships between age and cognitive performance, and (3) explore differences in neural flexibility between rest and task. Participants in the study were 386 healthy adults between the ages of 20-80 who provided resting state and/or task-based (Matrix Reasoning) functional magnetic resonance imaging (fMRI) scan data as part of their participation in two ongoing studies of cognitive aging. Neural flexibility measures from both resting and task-based scans reflected the number of times each node changed network assignment, and were averaged both across the whole brain (global neural flexibility) as well as within ten somatosensory/cognitive networks. Results showed that neural flexibility was not related to participant age, and that task-based global neural flexibility, as well as task-based neural flexibility in several networks, tended to be negatively related to reaction times during the Matrix Reasoning task, however these effects did not survive strict multiple comparisons correction. Resting state neural flexibility was not significantly related to either participant age or cognitive performance. Additionally, no neural flexibility measures significantly moderated relationships between participant age and cognitive outcomes. Further, neural flexibility differed as a function of scan type, with resting state neural flexibility being significantly greater than task-based neural flexibility. Thus, neural flexibility measures computed during a cognitive task may be more meaningfully related to cognitive performance across the adult lifespan then resting state measures of neural flexibility.

YNIMG Journal 2022 Journal Article

Transfer learning for cognitive reserve quantification

• Xi Zhu
• Yi Liu
• Christian G. Habeck
• Yaakov Stern
• Seonjoo Lee
• for-the-Alzheimer's-Disease-Neuroimaging-Initiative

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

YNIMG Journal 2021 Journal Article

A framework for identification of a resting-bold connectome associated with cognitive reserve

• Yaakov Stern
• Eleanna Varangis
• Christian Habeck

The concept of cognitive reserve proposes that specific life experiences result in more flexible or resilient cognitive processing allowing some people to cope better with age- or disease-related brain changes than others. Imaging studies seeking to understand the neural implementation of cognitive reserve have most often used task-related fMRI studies. Using that approach, we recently described a task-invariant cognitive-reserve network whose expression correlated with IQ and that moderated between cortical thickness and cognitive performance. Here we sought to identify a pattern of resting BOLD connectivity related to cognitive reserve. We identified a connectome pattern whose connectivity correlated with IQ in both the derivation sample and a separate replication sample. The majority of the edges showing positive relationships with IQ implicate frontal regions. In the derivation sample, connectivity either moderated the relationship between mean cortical thickness and a set of cognitive outcomes or accounted for unique variance in cognitive performance after accounting for cortical thickness. In a replication sample we found that expression of this connectome correlated significantly with the primary endpoint of IQ, and also accounted for unique variance in cognitive performance beyond cortical thickness. Our findings represent an intermediate level of replication and are unlikely to have arisen purely by type-I error. This connectivity pattern therefore meets some of our theoretical criteria for a cognitive reserve-related network and provides insight into the neural implementation of cognitive reserve. Further, expression of this connectome could potentially be used as a direct measure of cognitive reserve, and as an outcome measure for intervention studies that seek to influence cognitive reserve. Future validation of and re-derivation of the pattern in expanded data sets by our and other groups will lead to further improved estimates of cognitive reserve in resting functional connectivity.

YNICL Journal 2019 Journal Article

Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes

• Yun Wang
• Chenxiao Xu
• Ji-Hwan Park
• Seonjoo Lee
• Yaakov Stern
• Shinjae Yoo
• Jong Hun Kim
• Hyoung Seop Kim

Accurate, reliable prediction of risk for Alzheimer's disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping-morphometry and structural connectomics-and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N = 211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer's Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features = 34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n = 60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparisons of the classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers showed the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.

YNIMG Journal 2018 Journal Article

A task-invariant cognitive reserve network

• Yaakov Stern
• Yunglin Gazes
• Qolomreza Razlighi
• Jason Steffener
• Christian Habeck

The concept of cognitive reserve (CR) can explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or disease-related brain changes. Epidemiologic evidence indicates that CR helps maintain performance in the face of pathology across multiple cognitive domains. We therefore tried to identify a single, “task-invariant” CR network that is active during the performance of many disparate tasks. In imaging data acquired from 255 individuals age 20–80 while performing 12 different cognitive tasks, we used an iterative approach to derive a multivariate network that was expressed during the performance of all tasks, and whose degree of expression correlated with IQ, a proxy for CR. When applied to held out data or forward applied to fMRI data from an entirely different activation task, network expression correlated with IQ. Expression of the CR pattern accounted for additional variance in fluid reasoning performance over and above the influence of cortical thickness, and also moderated between cortical thickness and reasoning performance, consistent with the behavior of a CR network. The identification of a task-invariant CR network supports the idea that life experiences may result in brain processing differences that might provide reserve against age- or disease-related changes across multiple tasks.

YNIMG Journal 2018 Journal Article

Reference ability neural networks and behavioral performance across the adult life span

• Christian Habeck
• Teal Eich
• Ray Razlighi
• Yunglin Gazes
• Yaakov Stern

To better understand the impact of aging, along with other demographic and brain health variables, on the neural networks that support different aspects of cognitive performance, we applied a brute-force search technique based on Principal Components Analysis to derive 4 corresponding spatial covariance patterns (termed Reference Ability Neural Networks –RANNs) from a large sample of participants across the age range. 255 clinically healthy, community-dwelling adults, aged 20–77, underwent fMRI while performing 12 tasks, 3 tasks for each of the following cognitive reference abilities: Episodic Memory, Reasoning, Perceptual Speed, and Vocabulary. The derived RANNs (1) showed selective activation to their specific cognitive domain and (2) correlated with behavioral performance. Quasi out-of-sample replication with Monte-Carlo 5-fold cross validation was built into our approach, and all patterns indicated their corresponding reference ability and predicted performance in held-out data to a degree significantly greater than chance level. RANN-pattern expression for Episodic Memory, Reasoning and Vocabulary were associated selectively with age, while the pattern for Perceptual Speed showed no such age-related influences. For each participant we also looked at residual activity unaccounted for by the RANN-pattern derived for the cognitive reference ability. Higher residual activity was associated with poorer brain-structural health and older age, but –apart from Vocabulary-not with cognitive performance, indicating that older participants with worse brain-structural health might recruit alternative neural resources to maintain performance levels.

YNIMG Journal 2017 Journal Article

Cognitive neuroscience neuroimaging repository for the adult lifespan

• Qolamreza R. Razlighi
• Christian Habeck
• Daniel Barulli
• Yaakov Stern

With recent advances in neuroimaging technology, it is now possible to image human brain function in vivo, which revolutionized the cognitive neuroscience field. However, like any other newly developed technique, the acquisition of neuroimaging data is costly and logistically challenging. Furthermore, studying human cognition requires acquiring a large amount of neuroimaging data, which might not be feasible to do by every researcher in the field. Here, we describe our group's efforts to acquire one of the largest neuroimaging datasets that aims to investigate the neural substrates of age-related cognitive decline, which will be made available to share with other investigators. Our neuroimaging repository includes up to 14 different functional images for more than 486 subjects across the entire adult lifespan in addition to their 3 structural images. Currently, data from 234 participants have been acquired, including all 14 functional and 3 structural images, which is planned to increased to 375 participants in the next few years. A complete battery of neuropsychological tests was also administered to all participants. The neuroimaging and accompanying psychometric data will be available through an online and easy-to-use data sharing website.

YNIMG Journal 2016 Journal Article

Selective association between cortical thickness and reference abilities in normal aging

• Seonjoo Lee
• Christian Habeck
• Qolamreza Razlighi
• Timothy Salthouse
• Yaakov Stern

A previous study of reference abilities and cortical thickness reported that association between reference abilities and cortical thickness summarized over large ROIs suppressed was suppressed after controlling for mean cortical thickness and global cognition. In this manuscript, we showed that preserving detailed spatial patterns of cortical thickness can identify reference-ability-specific association besides the association explained by global cognition and mean cortical thickness. We identified associations between cortical thickness and 3 cognitive reference abilities after controlling for mean thickness, global cognition, and linear chronological age: (1) memory, (2) perceptual speed, and (3) vocabulary. Global cognition was correlated with mean overall thickness but also was found to have a regionally specific pattern of associations. Nonlinear associations between cortical thickness and cognition were not observed, neither were nonlinear age effects. Age-by-thickness interactions were also absent. This implies that all thickness–cognition relations and age associations are independent of age and that consequently no age range is inherently special, since brain-behavioral findings are invariant across the whole age range.

YNIMG Journal 2016 Journal Article

White matter tract covariance patterns predict age-declining cognitive abilities

• Yunglin Gazes
• F. DuBois Bowman
• Qolamreza R. Razlighi
• Deirdre O'Shea
• Yaakov Stern
• Christian Habeck

Previous studies investigating the relationship of white matter (WM) integrity to cognitive abilities and aging have either focused on a global measure or a few selected WM tracts. Ideally, contribution from all of the WM tracts should be evaluated at the same time. However, the high collinearity among WM tracts precludes systematic examination of WM tracts simultaneously without sacrificing statistical power due to stringent multiple-comparison corrections. Multivariate covariance techniques enable comprehensive simultaneous examination of all WM tracts without being penalized for high collinearity among observations. Method In this study, Scaled Subprofile Modeling (SSM) was applied to the mean integrity of 18 major WM tracts to extract covariance patterns that optimally predicted four cognitive abilities (perceptual speed, episodic memory, fluid reasoning, and vocabulary) in 346 participants across ages 20 to 79years old. Using expression of the covariance patterns, age-independent effects of white matter integrity on cognition and the indirect effect of WM integrity on age-related differences in cognition were tested separately, but inferences from the indirect analyses were cautiously made given that cross-sectional data set was used in the analysis. Results A separate covariance pattern was identified that significantly predicted each cognitive ability after controlling for age except for vocabulary, but the age by WM covariance pattern interaction was not significant for any of the three abilities. Furthermore, each of the patterns mediated the effect of age on the respective cognitive ability. A distinct set of WM tracts was most influential in each of the three patterns. The WM covariance pattern accounting for fluid reasoning showed the most number of influential WM tracts whereas the episodic memory pattern showed the least number. Conclusion Specific patterns of WM tracts make significant contributions to the age-related differences in perceptual speed, episodic memory, and fluid reasoning but not vocabulary. Other measures of brain health will need to be explored to reveal the major influences on the vocabulary ability.

YNIMG Journal 2014 Journal Article

The Reference Ability Neural Network Study: Motivation, design, and initial feasibility analyses

• Yaakov Stern
• Christian Habeck
• Jason Steffener
• Daniel Barulli
• Yunglin Gazes
• Qolamreza Razlighi
• Danielle Shaked
• Timothy Salthouse

We introduce and describe the Reference Ability Neural Network Study and provide initial feasibility data. Based on analyses of large test batteries administered to individuals ranging from young to old, four latent variables, or reference abilities (RAs) that capture the majority of the variance in age-related cognitive change have been identified: episodic memory, fluid reasoning, perceptual speed, and vocabulary. We aim to determine whether spatial fMRI networks can be derived that are uniquely associated with the performance of each reference ability. We plan to image 375 healthy adults (50 per decade from age 20 to 50; 75 per decade from age 50 to 80) while performing a set of 12 cognitive tasks. Data on 174 participants are reported here. Three tasks were grouped a priori into each of the four reference ability domains. We first assessed to what extent both cognitive task scores and activation patterns readily show convergent and discriminant validity, i. e. increased similarity between tasks within the same domain and decreased similarity between tasks between domains, respectively. Block-based time-series analysis of each individual task was conducted for each participant via general linear modeling. We partialled activation common to all tasks out of the imaging data. For both test scores and activation topographies, we then calculated correlations for each of 66 possible pairings of tasks, and compared the magnitude of correlation of tasks within reference ability domains to that of tasks between domains. For the behavioral data, globally there were significantly stronger inter-task correlations within than between domains. When examining individual abilities, 3 of the domains also met these criteria but memory reached only borderline significance. Overall there was greater topographic similarity within reference abilities than between them (p<0. 0001), but when examined individually, statistical significance was reached only for episodic memory and perceptual speed. We then turned to a multivariate technique, linear indicator regression analysis, to derive four unique linear combinations of Principal Components (PC) of imaging data that were associated with each RA. We investigated the ability of the identified PCs to predict the reference domain associated with the activation of individual subjects for individual tasks. Median accuracy rates for associating component task activation with a particular reference ability were quite good: memory: 82%; reasoning: 87%; speed: 84%; vocabulary: 77%. These results demonstrate that even using basic GLM analysis, the topography of activation of tasks within a domain is more similar than tasks between domains. The follow-up regression analyses suggest that all tasks with each RA rely on a common network, unique to that RA. Our ultimate goal is to better characterize these RA neural networks and then study how their expression changes across the age span. Our hope is that by focusing on these networks associated with key features of cognitive aging, as opposed to task-related activation associated with individual tasks, we will be able to advance our knowledge regarding the key brain changes that underlie cognitive aging.

YNIMG Journal 2010 Journal Article

Investigating hemodynamic response variability at the group level using basis functions

• Jason Steffener
• Matthias Tabert
• Aaron Reuben
• Yaakov Stern

Introduced is a general framework for performing group-level analyses of fMRI data using any basis set of two functions (i. e. , the canonical hemodynamic response function and its first derivative) to model the hemodynamic response to neural activity. The approach allows for flexible implementation of physiologically based restrictions on the results. Information from both basis functions is used at the group level and the limitations avoid physiologically ambiguous or implausible results. This allows for investigation of specific BOLD activity such as hemodynamic responses peaking within a specified temporal range (i. e. , 4–5 s). The general nature of the presented approach allows for applications using basis sets specifically designed to investigate various physiologic phenomena, i. e. , age-related variability in poststimulus undershoot, hemodynamic responses measured with cerebral blood flow imaging, or subject-specific basis sets. An example using data from a group of healthy young participants demonstrates the methods and the specific steps to study poststimulus variability are discussed. The approach is completely implemented within the general linear model and requires minimal programmatic calculations.

YNIMG Journal 2008 Journal Article

Global familiarity of visual stimuli affects repetition-related neural plasticity but not repetition priming

• Anja Soldan
• Eric Zarahn
• H. John Hilton
• Yaakov Stern

In this study, we tested the prediction of the component process model of priming [Henson, R. N. (2003). Neuroimaging studies of priming. Prog Neurobiol, 70 (1), 53–81] that repetition priming of familiar and unfamiliar objects produces qualitatively different neural repetition effects. In an fMRI study, subjects viewed four repetitions of familiar objects and globally unfamiliar objects with familiar components. Reliable behavioral priming occurred for both item types across the four presentations and was of a similar magnitude for both stimulus types. The imaging data were analyzed using multivariate linear modeling, which permits explicit testing of the hypothesis that the repetition effects for familiar and unfamiliar objects are qualitatively different (i. e. , non-scaled versions of one another). The results showed the presence of two qualitatively different latent spatial patterns of repetition effects from presentation 1 to presentation 4 for familiar and unfamiliar objects, indicating that familiarity with an object’s global structural, semantic, or lexical features is an important factor in priming-related neural plasticity. The first latent spatial pattern strongly weighted regions with a similar repetition effect for both item types. The second pattern strongly weighted regions contributing a repetition suppression effect for the familiar objects and repetition enhancement for the unfamiliar objects, particularly the posterior insula, superior temporal gyrus, precentral gyrus, and cingulate cortex. This differential repetition effect might reflect the formation of novel memory representations for the unfamiliar items, which already exist for the familiar objects, consistent with the component process model of priming.

YNIMG Journal 2008 Journal Article

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

• Christian Habeck
• Norman L. Foster
• Robert Perneczky
• Alexander Kurz
• Panagiotis Alexopoulos
• Robert A. Koeppe
• Alexander Drzezga
• Yaakov Stern

We performed univariate and multivariate discriminant analysis of FDG–PET scans to evaluate their ability to identify Alzheimer's disease (AD). FDG–PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker's diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures.

YNIMG Journal 2006 Journal Article

Distinct spatial patterns of brain activity associated with memory storage and search

• Eric Zarahn
• Brian C. Rakitin
• Diane Abela
• Joseph Flynn
• Yaakov Stern

The time it takes for a human participant to decide whether a given stimulus is an element of a remembered set increases approximately linearly with the number of elements in the set. Here we tested for and detected a spatial pattern of brain activity whose magnitude of expression during this memory search process correlates with set size. We then tested the idea that memory search simply involves a re-activation of neurons involved in remembering the set by statistically comparing the patterns of brain activity corresponding to memory search and set size dependent working memory maintenance. These patterns were significantly different, suggesting that memory search and working memory maintenance are mediated by distinct neural mechanisms.

YNIMG Journal 2004 Journal Article

Covariance PET patterns in early Alzheimer's disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance

• Nikolaos Scarmeas
• Christian G. Habeck
• Eric Zarahn
• Karen E. Anderson
• Aileen Park
• John Hilton
• Gregory H. Pelton
• Matthias H. Tabert

Although multivariate analytic techniques might identify diagnostic patterns that are not captured by univariate methods, they have rarely been used to study the neural correlates of Alzheimer's disease (AD) or cognitive impairment. Nonquantitative H2 15O PET scans were acquired during rest in 17 probable AD subjects selected for mild severity [mean-modified Mini Mental Status Examination (mMMS) 46/57; SD 5. 1], 16 control subjects (mMMS 54; SD 2. 5) and 23 subjects with minimal to mild cognitive impairment but no dementia (mMMS 53; SD 2. 8). Expert clinical reading had low success in discriminating AD and controls. There were no significant mean flow differences among groups in traditional univariate SPM Noxel-wise analyses or region of interest (ROI) analyses. A covariance pattern was identified whose mean expression was significantly higher in the AD as compared to controls (P = 0. 03; sensitivity 76–94%; specificity 63–81%). Sites of increased concomitant flow included insula, cuneus, pulvinar, lingual, fusiform, superior occipital and parahippocampal gyri, whereas decreased concomitant flow was found in cingulate, inferior parietal lobule, middle and inferior frontal, supramarginal and precentral gyri. The covariance analysis-derived pattern was then prospectively applied to the cognitively impaired subjects: as compared to subjects with Clinical Dementia Rating (CDR) = 0, subjects with CDR = 0. 5 had significantly higher mean covariance pattern expression (P = 0. 009). Expression of this pattern correlated inversely with Selective Reminding Test total recall (r = −0. 401, P = 0. 002), delayed recall (r = −0. 351, P = 0. 008) and mMMS scores (r = −0. 401, P = 0. 002) in all three groups combined. We conclude that patients with AD may differentially express resting cerebral blood flow covariance patterns even at very early disease stages. Significant alterations in expression of resting flow covariance patterns occur even for subjects with cognitive impairment. Expression of covariance patterns correlates with cognitive and functional performance measures, holding promise for meaningful associations with underlying biopathological processes.

YNIMG Journal 2003 Journal Article

Cognitive reserve modulates functional brain responses during memory tasks: a PET study in healthy young and elderly subjects

• Nikolaos Scarmeas
• Eric Zarahn
• Karen E Anderson
• John Hilton
• Joseph Flynn
• Ronald L Van Heertum
• Harold A Sackeim
• Yaakov Stern

Cognitive reserve (CR) is the ability of an individual to cope with advancing brain pathology so that he remains free of symptomatology. Epidemiological evidence and in vivo neurometabolic data suggest that CR may be mediated through education or IQ. The goal of this study was to investigate CR-mediated differential brain activation in 17 healthy young adults and 19 healthy elders. Using nonquantitative H2 15O PET scanning, we assessed relative regional cerebral blood flow while subjects performed a serial recognition memory task under two conditions: nonmemory control (NMC), in which one shape was presented in each study trial; and titrated demand (TD), in which study list length was adjusted so that each subject recognized shapes at approximately 75% accuracy. A factor score that summarized years of education and scores on two IQ indices was used as an index of CR. Voxel-wise, multiple regression analyses were performed with TD minus NMC difference PET counts as the dependent variable and the CR variable as the independent variable of interest. We identified brain regions where regression slopes were different from zero in each separate group, and also those where regression slopes differed between the two age groups. The slopes were significantly more positive in the young in the right inferior temporal gyrus, right postcentral gyrus, and cingulate, while the elderly had a significantly more positive slope in left cuneus. Brain regions where systematic relationships between CR and brain activation differ as a function of aging are loci where compensation for aging has occurred. They may mediate differential ability to cope with brain changes in aging.

YNIMG Journal 2003 Journal Article

Relation of cognitive reserve and task performance to expression of regional covariance networks in an event-related fMRI study of nonverbal memory☆

• Christian Habeck
• H.John Hilton
• Eric Zarahn
• Joseph Flynn
• James Moeller
• Yaakov Stern

Cognitive reserve (CR) has been established as a mechanism that can explain individual differences in the clinical manifestation of neural changes associated with aging or neurodegenerative diseases. CR may represent individual differences in how tasks are processed (i. e. , differences in the component processes), or in the underlying neural circuitry (of the component processes). CR may be a function of innate differences or differential life experiences. To investigate to what extent CR can account for individual differences in brain activation and task performance, we used fMRI to image healthy young individuals while performing a nonverbal memory task. We used IQ estimates as a proxy for CR. During both study and test phase of the task, we identified regional covariance patterns whose change in subject expression across two task conditions correlated with performance and CR. Common brain regions in both activation patterns were suggestive of a brain network previously found to underlie overt and covert shifts of spatial attention. After partialing out the influence of task performance variables, this network still showed an association with the CR, i. e. , there were reserve-related physiological differences that presumably would persist were there no subject differences in task performance. This suggests that this network may represent a neural correlate of CR.

YNIMG Journal 1998 Journal Article

fMRI analysis of the hippocampal subregions in age-related memory decline and Alzheimer's disease

• Scott A. Small
• Gerard M. Perera
• Robert DeLaPaz
• Yaakov Stern