Functional connectivity with the retrosplenial cortex predicts cognitive aging in rats

Ash, Jessica A.; Lu, Hanbing; Taxier, Lisa R.; Long, Jeffrey M.; Yang, Yihong; Stein, Elliot A.; Rapp, Peter R.

doi:10.1073/pnas.1525309113

Cited by 79 publications

(73 citation statements)

References 66 publications

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“…Future studies can use the individual functional connectome to predict the effect of causal control similar to the way human resting-state fMRI is used to predict individual task fMRI activation pattern 9 , linking the different levels of organization and uncovering sources of individual variation. In addition, rodent models of disease are commonly studied using fcMRI 21,22,61 , which allows direct translation to humans 20,28 , as well as studying the relations between functional connectivity and behavior [23][24][25][26] . Such studies can utilize the approach presented here to follow the trajectory of individual animals during development, aging or after treatment, as well as the CPM, which provide a data-driven alternative to predict behavior based on fcMRI data 32 , instead of following a hypothesis-driven behavioral correlations of functional connections between specific seed regions.…”

Section: Discussionmentioning

confidence: 99%

“…Such investigation demands adequate sample size that is hard to achieve in studies in non-human primates and may involve genetic manipulations and molecular techniques that are more readily accessible in rodent models, and specifically in mice. Previous fcMRI studies in anesthetized mice demonstrated reproducible resting-state networks 18,19 , applications to mouse models of neuropsychiatric diseases [20][21][22] , and correlations between functional connectivity and behavioral measures [23][24][25][26] . However, characterization of individual differences in functional connectivity is based on repeated data acquisition that can control for measurement instability.…”

Section: Introductionmentioning

confidence: 99%

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Individual variability in functional connectivity architecture of the mouse brain

Bergmann

Gofman

Kavushansky

et al. 2020

Preprint

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The functional organization of brain networks can be estimated using fMRI by examining the coherence of spontaneous fluctuations in the fMRI signal, a method known as resting-state functional connectivity MRI. Previous studies in humans reported that such functional networks are dominated by stable group and individual factors, demonstrating that fMRI is suited to measuring subject-specific characteristics, and suggesting the utility of such precision fMRI approach in personalized medicine. However, mechanistic investigations to the sources of individual variability in health and disease are limited in humans and thus require animal models.Here, we used repeated-measurement resting-state fMRI in awake mice to quantify the contribution of individual variation to the functional architecture of the mouse cortex. Comparing the organization of functional networks across the group, we found dominant common organizational principles. The data also revealed stable individual features, which create a unique fingerprint that allow identification of individual mice from the group. Examining the distribution of individual variation across the mouse cortex, we found it is homogeneously distributed in both sensory and association networks. Finally, connectome-based predictive modeling of motor behavior in the rotarod task revealed that individual variation in functional connectivity explained behavioral variability. Collectively, these results show that mouse functional networks are characterized by individual variations suggesting that individual variation characterizes the mammalian cortex in general, and not only the primate cortex. These findings lay the foundation for future mechanistic investigations of individual brain organization and pre-clinical studies of brain disorders in the context of personalized medicine.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Individual variability in functional connectivity architecture of the mouse brain

Bergmann

Gofman

Kavushansky

et al. 2020

Preprint

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show abstract

“…During a memory task in older adults, the functional connectivity of the DMN and hippocampus is correlated with memory ability (Li et al, 2015). A DMN has also been described in rodents that exhibit disrupted functional connectivity in cognitively impaired compared to unimpaired aged animals (Ash et al, 2016). Future studies in which the effects of genetic and pharmacological manipulations on DMN activity are determined may reveal the cellular and molecular mechanisms underlying age-related disruption of functional connectivity of neuronal networks.…”

Section: Cellular and Molecular Hallmarks Of Brain Agingmentioning

confidence: 99%

Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States

2018

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During aging, the cellular milieu of the brain exhibits tell-tale signs of compromised bioenergetics, impaired adaptive neuroplasticity and resilience, aberrant neuronal network activity, dysregulation of neuronal Ca2+ homeostasis, the accrual of oxidatively modified molecules and organelles, and inflammation. These alterations render the aging brain vulnerable to Alzheimer’s and Parkinson’s diseases and stroke. Emerging findings are revealing mechanisms by which sedentary overindulgent lifestyles accelerate brain aging, whereas lifestyles that include intermittent bioenergetic challenges (exercise, fasting, and intellectual challenges) foster healthy brain aging. Here we provide an overview of the cellular and molecular biology of brain aging, how those processes interface with disease-specific neurodegenerative pathways, and how metabolic states influence brain health.

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“…Notably, there is significant variability in the severity of age‐related memory decline experienced between individuals, which may be due in part to differential effects of aging on the physiological and neurobiological processes in the hippocampus (Ash et al, ; Gallagher et al, ; Rapp & Amaral, ; Stark, Yassa, & Stark, ; Tomás Pereira, Gallagher, & Rapp, ). Critically, individual differences in the trajectory of age‐related changes in cognition and neural systems may be influenced by modifiable protective factors such as physical activity (PA; Hayes et al, ; Suwabe et al, , ) and cardiorespiratory fitness (CRF), which is influenced largely by genetics and PA (Hayes, Forman, & Verfaellie, ; Hayes, Hayes, Williams, Liu, & Verfaellie, ).…”

Section: Introductionmentioning

confidence: 99%

Cardiorespiratory fitness and hippocampal volume predict faster episodic associative learning in older adults

et al. 2019

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Declining episodic memory is common among otherwise healthy older adults, in part due to negative effects of aging on hippocampal circuits. However, there is significant variability between individuals in severity of aging effects on the hippocampus and subsequent memory decline. Importantly, variability may be influenced by modifiable protective physiological factors such as cardiorespiratory fitness (CRF). More research is needed to better understand which aspects of cognition that decline with aging benefit most from CRF. The current study evaluated the relation of CRF with learning rate on the episodic associative learning (EAL) task, a task designed specifically to target hippocampal‐dependent relational binding and to evaluate learning with repeated occurrences. Results show higher CRF was associated with faster learning rate. Larger hippocampal volume was also associated with faster learning rate, though hippocampal volume did not mediate the relationship between CRF and learning rate. Furthermore, to support the distinction between learning item relations and learning higher‐order sequences, which declines with aging but is largely reliant on extra‐hippocampal learning systems, we found learning rate on the EAL task was not related to motor sequence learning on the alternating serial reaction time task. Motor sequence learning was also not correlated with hippocampal volume. Thus, for the first time, we show that both higher CRF and larger hippocampal volume in healthy older adults are related to enhanced rate of relational memory acquisition.

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Functional connectivity with the retrosplenial cortex predicts cognitive aging in rats

Cited by 79 publications

References 66 publications

Individual variability in functional connectivity architecture of the mouse brain

Individual variability in functional connectivity architecture of the mouse brain

Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States

Cardiorespiratory fitness and hippocampal volume predict faster episodic associative learning in older adults

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