Prefrontal-cingulate functional networks in aging monkeys: neural circuit substrates of cognitive aging

衰老猴子的前额叶-扣带回功能网络：认知衰老的神经回路基质

• 批准号: 10726860
• 负责人: Bang-Bon Koo
• 金额: $ 233.09万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10726860
• 关键词: Adult; Affect; Affective; Age; Age-associated memory impairment; Aging; Alzheimer' s disease related dementia; Anatomy; Anterior; Architecture; Area; Behavior; Behavioral; Biological Markers; Brain; Categories; Cells; Cognition; Cognitive; Cognitive aging; Computer Models; Data; Data Set; Development; Diffusion Magnetic Resonance Imaging; Dorsal; Electrophysiology (science); Equilibrium; FOS gene; Functional Imaging; Functional Magnetic Resonance Imaging; Functional disorder; Genes; Glutamates; Hour; Human; Image; Immunolabeling Technics; Impaired cognition; In Vitro; Injections; Label; Lateral; Longitudinal Studies; Macaca mulatta; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Measures; Medial; Mediating; Memory; Methods; Microscopy; Modeling; Molecular; Molecular Profiling; Monkeys; Motor; Myelin; Neurodegenerative Disorders; Neurons; Pathway interactions; Pattern; Performance; Play; Prefrontal Cortex; Property; Resolution; Rest; Role; Sampling; Sensory; Shapes; Short-Term Memory; Signal Transduction; Synapses; System; Testing; Tissue Harvesting; Tracer; Validation; age related; aged; cingulate cortex; cognitive performance; cognitive task; cognitive testing; computer framework; density; excitatory neuron; flexibility; frontal lobe; gamma-Aminobutyric Acid; imaging biomarker; in vivo; inhibitory neuron; machine learning framework; magnetic resonance imaging biomarker; multimodality; network dysfunction; network models; neural; neural circuit; neural network; neural tract; neurochemistry; neuropsychiatric disorder; normal aging; novel; single nucleus RNA-sequencing; transcriptomics; white matter; young adult

Abstract White matter degeneration and synapse loss in the frontal lobe are the most pronounced neural changes associated with age-related cognitive decline , but the extent to which these changes affect anatomical neural circuits and network dynamics in vivo are largely unknown. In recent human studies, correlated functional MRI BOLD signal fluctuations found across anatomically related brain areas have been used to infer functional “resting-state” network (RSN) connectivity, providing a promising non-invasive means to study the dynamics of brain networks in vivo . Deciphering the relationships between age-related changes in in vivo whole-brain network activity and the cellular-molecular architecture is key to understanding cognitive aging and neurodegenerative disease, such as Alzheimer’s Disease and Related Dementias (ADRD), in the human brain. The focus of this project is on functionally-distinct prefrontal areas implicated in cognition and aging-- the lateral prefrontal cortex (LPFC),) and medial prefrontal anterior cingulate cortex (ACC)—which participate in distinct functional networks and have diverse roles in cognitive processing. The ACC in particular has been implicated in controlling functional network states and RSN whole brain dynamics to mediate flexible behavior, but the exact mechanisms are not fully understood. The excitatory and inhibitory microcircuits within distinct prefrontal areas, and their extrinsic pathways likely play an important role in shaping the dynamics of functional connectivity. However, in depth anatomical and neurochemical cellular and molecular validation of in vivo MRI measures of connectivity and microstructural integrity and how these properties change with age have not been assessed. This proposal aims to unravel the properties of these anatomical circuits and their relationships with age-related changes in RSN dynamics and cognitive decline in our well-established rhesus monkey model of normal aging. Using multimodal MRI and cognitive testing, combined with single-cell transcriptomics, in vitro electrophysiology, neural tract- tracing and high-resolution microscopy in young and aged adult rhesus monkeys (Macaca mulatta), we will highlight features of age-related changes in distinct RSNs and their relationship to white matter integrity, excitatory-inhibitory circuitry and extrinsic pathways in LPFC and ACC. We will use this large-scale multi-modal MRI and microstructural dataset to build a computational machine learning framework that can define biomarkers of cognitive aging. The overall hypothesis of this proposal is that functional and structural network connectivity and excitatory:inhibitory (E:I) balance of LPFC and ACC areas are differentially altered with age, in patterns that underlie specific aspects of age-related cognitive decline. The proposed multimodal studies will yield data that can be used to infer the neural substrates of human brain networks and predict how they change across age. These data will inform development of novel functional imaging biomarkers for neural circuit dysfunctions associated with cognitive impairments in aging, which can be extended to related neuropsychiatric and neurodegenerative diseases.

摘要