Longitudinal imaging pattern analysis (SPARE-CD index) detects early structural and functional changes before cognitive decline in healthy older adults

Clark, Vanessa; Resnick, Susan M.; Doshi, Jimit; Beason‐Held, Lori L.; Zhou, Yun; Ferrucci, Luigi; Wong, Dean F.; Kraut, Michael A.; Davatzikos, Christos

doi:10.1016/j.neurobiolaging.2012.01.010

Cited by 33 publications

(29 citation statements)

References 60 publications

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“…Notably, we found that QA injections into mPFC actually increased FC strength between various cortical regions, which might in fact mimic the early, excitatory stages of Alzheimer-like neuropathology70. One of the advantages of the rsfMRI methodology is its ability to detect functional alterations that occur much earlier than structural changes377172. The reversal learning defects and spatial-cognitive rigidity, which we observed in our mPFC-lesioned mice, coincided with widespread changes in cortical connectivity.…”

Section: Discussionsupporting

confidence: 53%

Quinolinic acid injection in mouse medial prefrontal cortex affects reversal learning abilities, cortical connectivity and hippocampal synaptic plasticity

Latif‐Hernandez

Shah

Ahmed

et al. 2016

Sci Rep

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Intracerebral injection of the excitotoxic, endogenous tryptophan metabolite, quinolinic acid (QA), constitutes a chemical model of neurodegenerative brain disease. Complementary techniques were combined to examine the consequences of QA injection into medial prefrontal cortex (mPFC) of C57BL6 mice. In accordance with the NMDAR-mediated synapto- and neurotoxic action of QA, we found an initial increase in excitability and an augmentation of hippocampal long-term potentiation, converting within two weeks into a reduction and impairment, respectively, of these processes. QA-induced mPFC excitotoxicity impaired behavioral flexibility in a reversal variant of the hidden-platform Morris water maze (MWM), whereas regular, extended MWM training was unaffected. QA-induced mPFC damage specifically affected the spatial-cognitive strategies that mice use to locate the platform during reversal learning. These behavioral and cognitive defects coincided with changes in cortical functional connectivity (FC) and hippocampal neuroplasticity. FC between various cortical regions was assessed by resting-state fMRI (rsfMRI) methodology, and mice that had received QA injection into mPFC showed increased FC between various cortical regions. mPFC and hippocampus (HC) are anatomically as well as functionally linked as part of a cortical network that controls higher-order cognitive functions. Together, these observations demonstrate the central functional importance of rodent mPFC as well as the validity of QA-induced mPFC damage as a preclinical rodent model of the early stages of neurodegeneration.

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Section: Discussionsupporting

confidence: 53%

Quinolinic acid injection in mouse medial prefrontal cortex affects reversal learning abilities, cortical connectivity and hippocampal synaptic plasticity

Latif‐Hernandez

Shah

Ahmed

et al. 2016

Sci Rep

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“…Thus severity can be estimated by calculating the distance of each subject from the discriminating hyperplane. Such an approach has been been used in prior studies to quantify severity of the disorder, such as Alzhiemers’ and Mild Cognitive Impairment (Davatzikos et al, 2009; Clark et al, 2012) and Autism Spectrum Disorder (Ingalhalikar et al, 2011). Currently, MOE provides information about heterogeneity alone.…”

Section: Discussionmentioning

confidence: 99%

Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging

Eavani

Hsieh

et al. 2016

NeuroImage

Self Cite

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In MRI studies, linear multi-variate methods are often employed to identify regions or connections that are affected due to disease or normal aging. Such linear models inherently assume that there is a single, homogeneous abnormality pattern that is present in all affected individuals. While kernel-based methods can implicitly model a non-linear effect, and therefore the heterogeneity in the affected group, extracting and interpreting information about affected regions is difficult. In this paper, we present a method that explicitly models and captures heterogeneous patterns of change in the affected group relative to a reference group of controls. For this purpose, we use the Mixture-Of-Experts (MOE) framework, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers. MOE approximates the non-linear boundary between the two groups with a piecewise linear boundary, thus allowing discovery of multiple patterns of group differences. In the case of patient/control comparisons, each such pattern aims to capture a different dimension of a disease, and hence to identify patient subgroups. We validated our model using multiple simulation scenarios and performance measures. We applied this method to resting state functional MRI data from the Baltimore Longitudinal Study of Aging, to investigate heterogeneous effects of aging on brain function in cognitively normal older adults (> 85 years) relative to a reference group of normal young to middle-aged adults (< 60 years). We found strong evidence for the presence of two subgroups of older adults, with similar age distributions in each subgroup, but different connectivity patterns associated with aging. While both older subgroups showed reduced functional connectivity in the Default Mode Network (DMN), increases in functional connectivity within the pre-frontal cortex as well as the bilateral insula were observed only for one of the two subgroups. Interestingly, the subgroup showing this increased connectivity (unlike the other subgroup) was, cognitively similar at baseline to the young and middle-aged subjects in two of seven cognitive domains, and had a faster rate of cognitive decline in one of seven domains. These results suggest that older individuals whose baseline cognitive performance is comparable to that of younger individuals recruit their “cognitive reserve” later in life, to compensate for reduced connectivity in other brain regions.

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“…That is, what are the “normal” age effects on cognition? Previous work has clearly demonstrated that both brain atrophy and decline in neuropsychological test performance are key features of normal aging (Lamar et al, 2003; Resnick et al, 2003), and that the earliest observed changes are in structural and functional measures of the brain (Clark et al, 2012; Saykin et al, 2006). Yet, it is less clear whether such brain changes, and the previously highlighted fluid AD biomarker changes, fully explain age-related cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Alzheimer's disease biomarkers as mediators of age-related cognitive decline

Hohman

Tommet

Marks

et al. 2017

Neurobiology of Aging

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Age-related changes in cognition are partially mediated by the presence of neuropathology and neurodegeneration. This manuscript evaluates the degree to which biomarkers of Alzheimer’s disease (AD) neuropathology and longitudinal changes in brain structure account for age-related differences in cognition. Data from the AD Neuroimaging Initiative (n=1012) were analyzed, including individuals with normal cognition and mild cognitive impairment. Parallel process mixed effects regression models characterized longitudinal trajectories of cognitive variables and time varying changes in brain volumes. Baseline age was associated with both memory and executive function at baseline (p’s<0.001), and change in memory and executive function performance over time (p’s<0.05). After adjusting for clinical diagnosis, baseline and longitudinal change in brain volume, and baseline levels of CSF biomarkers, age effects on change in episodic memory and executive function were fully attenuated, age effects on baseline memory were substantially attenuated, but an association remained between age and baseline executive function. Results support previous studies that show that age effects on cognitive decline are fully mediated by disease and neurodegeneration variables but also show domain specific age effects on baseline cognition, specifically an age pathway to executive function that is independent of brain and disease pathways.

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Longitudinal imaging pattern analysis (SPARE-CD index) detects early structural and functional changes before cognitive decline in healthy older adults

Cited by 33 publications

References 60 publications

Quinolinic acid injection in mouse medial prefrontal cortex affects reversal learning abilities, cortical connectivity and hippocampal synaptic plasticity

Quinolinic acid injection in mouse medial prefrontal cortex affects reversal learning abilities, cortical connectivity and hippocampal synaptic plasticity

Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging

Evaluating Alzheimer's disease biomarkers as mediators of age-related cognitive decline

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