Overview

• Instead of taking a deep dive into a single cognitive domain, we focus multiple cognitive domains in the same individuals – namely, learning, memory, executive function/attention, and perceptual-motor speed. 

• We take a similarly multifaceted neuroimaging approach. We study age-related differences in functional MRI measures of brain function, diffusion-weighted imaging measures (DWI) of brain microstructure, and quantitative susceptibility mapping measures of brain iron. We employ cutting-edge techniques, including high-resolution multi-shot whole-brain DWI (≤ 1 mm isotropic voxels), and cortical column-based analyses that achieve a degree of anatomical specificity not commonly found in vivo.

• We also recruit adults across the entire lifespan, including those in midlife, where the earliest signs of cognitive decline may be observed, as well as the oldest-old (ages 85+ years). The oldest-old are the fastest growing segment of the global population and may represent a unique model for studying successful aging.

• And it is not just about characterizing healthy aging. We also compare neural signatures between healthy older adults and adults with Alzheimer’s disease to help differentiate normal aging from age-related neurodegenerative disease.  

(1) Can advanced neuroimaging techniques help better predict age-related cognitive decline?

Much of our work is motivated by the cortical disconnection model, which proposes that age-related cognitive dysfunction can be partly attributed to the degradation of the white matter pathways that connect distributed gray matter regions. Our lab tests whether this type of “disconnection” can be better detected by state-of-the-art analytical techniques (i.e., graph theoretical network analyses, cortical depth-wise analyses) and high spatial resolution MRI. These results have identified additional age- and AD-related differences in brain structure that would otherwise be missed by standard approaches, which will help answer fundamental questions about relations between brain structure and cognitive aging and assist with the early identification of adults at risk of atypical cognitive decline.

• Depth-wise analyses of cortical iron: One line of work has focused on the role of excess brain iron in cognitive aging (Madden & Merenstein, 2023, NeuroImage). We employ advanced cortical depth-wise analyses that provide a more fine-grained level of anatomical detail than standard voxel-wise or whole region-of-interest approaches. (Please note the term depth refers to equidistant partitions of cortex, rather than specific layers). Using this approach, we have found that, relative to healthy controls, adults with AD exhibit higher iron content across all cortical depths, especially for higher-order brain regions affected by AD-related neuropathologies (Merenstein et al., 2024, Cereb Cortex). We have further shown that this pattern is distinct from healthy aging, where superficial depths were more vulnerable to excess iron accumulation across the adult lifespan. This depth-specific difference in iron accumulation contributed to the age-related decline in general fluid cognition (Merenstein et al., 2025, Neurobio Aging).

• High-resolution MRI: Extant MRI techniques have been invaluable for understanding the aging brain but are often limited by their spatial resolution. We have tested whether a high spatial resolution diffusion-weighted imaging (DWI) protocol (1𝜇l volume) could better explain age-related cognitive decline than a standard protocol (3.375 𝜇l volume). In support of this notion, across the adult lifespan, only measures of gray matter microstructure and graph theoretical measures of white matter connectivity derived from the high-resolution sequence mediated age-related differences in cognitive performance, suggesting that this sequence may better identify adults at heightened risk of dementia (Merenstein et al., 2023, NeuroImage).

(2) Does cortical disconnection contribte to decline across different cognitive domains?

Instead of delving deep into a single cognitive domain, our work assesses the cortical disconnection model across multiple domains. Doing so is important as cortical disconnection should not be domain-specific. A line of functional MRI (fMRI) work supports the notion that our cognitive abilities are supported by distributed brain networks that become altered in healthy aging, including:

• Mnemonic discrimination: This crucial component of episodic memory allows us to differentiate between new and previously experienced events (e.g., taking medication today vs. yesterday). Many studies have attributed this ability to the hippocampus, but this deep brain structure is part of a brain-wide system that does not function in isolation. In a whole-brain fMRI study of younger adults, we reported some of the first evidence of memory-related activation in both the hippocampus and occipital cortex (Klippenstein et al., 2020, Brain Behav).

• Associative learning: This ability to form associations between events (e.g., social interactions, learning a new technology) declines in healthy aging and is associated with age-related increases in activation of cortico-hippocampal and cortico-striatal networks. However, we have shown that, although learning emerges later for older adults, they still engage similar functional networks as younger adults when forming associations between events (Merenstein et al., 2021, Behav Brain Res).

• Visual attention: This fundamental component of fluid cognition allows us to filter distracting information and focus on task-relevant information. Using fMRI in a sample of adults across the lifespan (ages 18-78 years), we have shown that frontoparietal activation contributes to age-related decline in the efficiency of visual attention processes during difficult search conditions (i.e., when target and nontarget items were highly similar; Merenstein et al., 2023, Atten Percept Psychophys).

(3) How is advanced brain aging (ages 80+ years) unique from brain aging in younger groups (ages 65-80)?

This work has provided compelling evidence for the cortical disconnection model across the adult lifespan and multiple cognitive domains. But for a theory to be fully valid, it needs to account for patterns observed in all age groups, including advanced age. However, these theoretical predictions based on empirical findings from younger-old adults may not generalize into advanced age, due to the higher prevalence of cognitive impairment and neuropathologies. Thus, we test whether this model generalizes to the oldest-old (80+ years) – the fastest growing segment of the population, who will inevitably become more represented in MRI studies of neurocognitive aging.

• Cortical disconnection: Using DWI across the entire older adult lifespan (ages 65-98 years), we have observed pronounced white matter microstructure degradation in the tenth decade of life, and degradation of medial temporal microstructure mediated age-related memory decline (Merenstein et al., 2021, Neurobiol Aging). We have also provided the first evidence of associative learning in the oldest-old, which was related to better white matter microstructure of the cortico-striatal network (Merenstein et al., 2023, Cogn Affect Behav Neurosci). Together, these studies provide evidence of cortical disconnection even into the ninth and tenth decades of life.  

• Other frameworks: To test whether other foundational theories also apply to advanced age, we have extensively reviewed MRI studies of the oldest-old (Merenstein et al., 2022, Neurosci Biobehav Rev). Although some theoretical predictions are valid across older adulthood (e.g., brain maintenance), others (e.g., compensation) may need to be modified to account for the unique cognitive and neural profiles of the oldest-old. This work highlights the heterogeneity of neurocognitive aging in this advanced age group and suggests that the inclusion of oldest-old adults may impact the accuracy of modern theoretical predictions.

Future Directions

By assessing the neurobiological substrates of cognition across the adult lifespan, our previous research has made important implications for several theories of neurocognitive aging. Going forward, we aim to assess, and improve, the utility of MRI for identifying adults at risk of atypical cognitive decline. Doing so will allow for earlier interventions aimed at slowing neurocognitive aging. However, an important impediment to this goal is that patterns of cognitive and brain aging are not fully consistent across the adult lifespan.

In one line of future work, we aim to address the question, how do the neural substrates underlying cognitive aging differ across the adult lifespan? We will capitalize on advanced, multimodal MRI techniques and the oldest-old population to better characterize age-related differences in various cognitive domains (memory, attention, perceptual-motor speed) and their neural substrates across the entire adult lifespan (ages 18-100+ years). A specific interest is whether certain aspects of cognition exhibit more accelerated or stable decline into the later stages of life, and whether they are supported by distinct neural substrates. For example, the degree of white matter tissue damage (i.e., white matter hyperintensities) appears to better explain cognitive performance in younger-old (ages 65-80 years) relative to oldest-old (ages 80+ years) adults (Merenstein et al., 2022, Neurosci Biobehav Rev). If so, then other MRI measures may be better suited for predicting atypical cognitive decline in advanced age. To test this, we will extend our prior cortical column-based analyses to other neural properties, including gray matter microstructure (assessed by diffusion-weighted imaging) and myelin (assessed by myelin mapping), and continue using graph theoretical analyses to characterize specific network properties. 

Additionally, we aim to address the question, how well do common structural MRI measures map onto their underlying neural substrates? As the oldest-old are nearing the end of their life expectancy, they offer the rare opportunity to assess the same neural properties in the same individuals in vivo and ex vivo. A particular interest lies comparing the results from my in vivo depth-wise analyses to their ex vivo neural counterparts at the layer level (Merenstein et al., 2024, Cereb Cortex; Merenstein et al., 2025, Neurobio Aging) and validating measures of tissue microstructure derived from biophysical models (e.g., Neurite Orientation Density and Dispersion Index; Merenstein et al., 2023, Cogn Affect Behav Neurosci). 

Ultimately, these lines of work will help answer fundamental questions about relations between brain aging and cognitive aging and identify additional age-related differences in brain structure and function that have been missed by standard MRI techniques.