Can advanced neuroimaging measures better explain cognitive aging than standard measures?
Much of my 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. In my work, I test 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: In one line of work, I focus on the role of excess brain iron in cognitive aging (Madden & Merenstein, 2023, NeuroImage). To assess brain iron, I employ novel 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, I have shown 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). I have further shown that this pattern is distinct from healthy aging, where only superficial depths were uniquely 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., under review).
High-resolution MRI: Extant MRI techniques have been invaluable for understanding the aging brain but are often limited by their spatial resolution. In my postdoctoral work, I capitalized on a novel, locally developed high spatial resolution diffusion-weighted imaging (DWI) sequence (1𝜇l volume) and tested whether it could better explain age-related cognitive decline than a standard protocol (3.375 𝜇l volume). 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).
Does cortical disconnection underlie decline across different cognitive domains?
Instead of delving deep into a single cognitive domain, my work assesses whether the cortical disconnection model is supported across multiple domains. Doing so is important as cortical disconnection should not be domain-specific. In support of this notion, I have published five functional MRI (fMRI) studies showing that our cognitive abilities are supported by distributed brain networks that become altered in healthy aging.
Memory and learning: Mnemonic discrimination is a crucial component of episodic memory that 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. To test whether memory is indeed supported by a brain-wide network, I conducted a whole-brain fMRI study of younger adults that reported some of the first evidence of memory-related activation in both the hippocampus and occipital cortex (Klippenstein et al., 2020, Brain Behav).
In other related work, I assessed differences in associative learning performance between younger and healthy older adults. I found that older adults exhibited increased activation of cortico-hippocampal and cortico-striatal networks during the early task stage, when an age-related learning deficit was present in the behavioral data. After matching the age groups on behavioral performance (i.e., early task stage for younger adults, later task stage for older adults), however, these differences in activation were no longer evident. These findings suggest that although learning emerges later for older adults, they are still engaging similar functional networks as younger adults when forming associations between events (Merenstein et al., 2021, Behav Brain Res).
Visual attention: Another fundamental component of fluid cognition is visual attention, which allows us to filter out distracting information and focus only on task-relevant information. In recent fMRI work, I studied the relation of frontoparietal activation to visual search performance. I used diffusion decision modeling to differentiate reaction time into two unique components: decision-making (drift rate) or sensorimotor (nondecision time) processes. Both processes exhibited decline across the adult lifespan (ages 18-78 years). Importantly, a novel finding was that when search was inefficient (i.e., when target and nontarget items were highly similar), frontoparietal activation contributed to age-related decline in drift rate. This study highlights the value of information processing models for understanding relations among cognition, aging, and brain function (Merenstein et al., 2023, Atten Percept Psychophys).
To what extent is advanced aging (ages 80+ years) unique from aging in younger groups (ages 65-80)?
My 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. In a separate line of work, I 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: My initial DWI publication was the first to assess age-related differences in white matter microstructure across the entire older adult lifespan (ages 65-98 years). I observed pronounced 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). In another DWI publication, I 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, I conducted an extensive review of MRI studies of the oldest-old (Merenstein et al., 2022, Neurosci Biobehav Rev). I found that some theoretical predictions are valid across older adulthood (e.g., brain maintenance), but others (e.g., compensation) may need to be modified to account for the unique cognitive and neural profiles of the oldest-old. These findings highlight the importance of my work as it considers the heterogeneity of neurocognitive aging in this advanced age group and will directly assess the impact of oldest-old adults on the accuracy of modern theoretical predictions.