Functional brain networks affected by aging, amyloidosis and Alzheimer’s disease risk factors
Although familial Alzheimer’s disease (AD) constitutes a minority of worldwide cases, such early onset heritable variants of AD are aggressive in their presentation and progression. The inherited genetic mutations affect amyloid precursor protein (APP) processing pathways. APP is a structural protein found at neuronal synapses and its aberrant processing is central to the formation of a toxic 42 amino acid long amyloid beta (Abeta) peptide. Increases in Abeta leads to a rise in amyloid plaques in the brain. Although a causal role of Abeta in AD remains unclear, it is a well known AD hallmark that interferes with neuronal activity in neocortical and parahippocampal circuits. There are substantial reports showing that Abeta can disrupt synaptic communication, alter excitatory-inhibitory balance, and impair long-term potentiation (LTP), a synaptic mechanisms underlying learning and memory. The reported synaptic effects of Abeta are in turn thought to interfere with larger scale neural communication, measured in fMRI studies. However, this is an area needing more research.
Brain Abeta accumulation is often accompanied by elevated microglia. Abeta and neuroinflammatory mechanisms go ‘hand-in-hand’, but the mechanisms by which these factors alter large scale networks remains unclear. Large scale connectivity is a critical endpoint of local circuit communication/information transfer, which ultimately underlies many keys aspects of cognition and emotion. Clarifying how neuronal-immune interactions shape functioanl connectivity networks is an important step towards understanding diseases such as AD and related dementia and neuropsychiatric conditions. Moreover, the role of other cells, such as astrocytes and oligodendrocytes, may also be important in re-shaping of functional brain networks as well, and in long-term deterioration of functional communication in the AD brain.
We use two major ways to interrogate functional brain networks in mouse models. Functional MRI datasets are typically collected in the AMRIS facility, using their dedicated preclinical 11.1 Tesla and 7 Tesla MRI scanners. Once collected, we have a wealth of data to interrogate brain wide function. One approach uses Independent Components Analysis (ICA) as a way to explore ‘what the data can tell us’ about the rodent brain. In other words, this model free approach uncovers disease-induced differences in well established ICA networks and can inform on functional changes that are specific to certain neural systems (e.g., default mode, salience, prefrontal, etc).
We also use a ‘seed’ based approach, generating many region of interest (ROI) masks over extended regions of the mouse cortex and subcortical areas. This ROI based approach is guided by the Allen Brain Institutes Common Coordinate Framework template and parcellation, which allows for comparisons across studies. Using this ROI approach we extract fMRI signals from many areas of the brain and following statistical correlation analysis, we assemble the pairwise correlation indices into weighted undirected adjacency matrices to be used in network analyses. Brain network analysis stems from the field of graph theory and can be used to measure brain wide organizational aspects of functional connectivity.
In addition to fMRI, our laboratory also uses diffusion weighted imaging (DWI) acquisitions that provide indices of brain tissue microstructure. Neurodegenerative changes in the brain can obstruct or hinder net water displacement in white matter and extracellular spaces. Such microstructural changes in the displacement of water can be detected to a certain degree in vivo by DWI. Postprocessing of DWI scans can offer a number of distinctly informative scalar maps, each providing an index of water directionality, lengthwise, radial and average diffusivities, and intracellular vs extracellular isotropic water components, and dispersion orientation of intracellular lengthwise diffusivity. Previous research has linked DWI signals such as these to brain inflammatory mechanisms.
We have a series of major research question of interest: (1) how does amyloidosis, either alone or in combination with other AD risk factors, affect functional connectivity and limbic-cognitive communication? (2) In turn, how does this affect cognitive and affective (emotional) behaviors? (3) How do the molecular-cellular actions of amyloid on neuronal /synaptic communication translate to larger scale brain network changes? (4) Are functional changes on the amyloid burdened brain linked to microstructural changes, and are these related to neuroinflammatory activity involving microglia, astrocytes, oligodendrocytes, and finally, (4) how are the functional changes with amyloidosis distinct from those produced by tauopathy.
Functional connectome remapping following brain contusive injuries
Among moderate-to-severe brain injuries, penetrative concussive cortical injury cause permanent structural and functional deficits in brain areas that regulate cognitive, sensorimotor, and affective functions. The degree of damage caused at and beyond the epicenter can determine the severity of cognitive impairment and/or neuropsychiatric sequelae. Degeneration of axons and dendrites can occur beyond the TBI foci and produce impairments in brain wide communication across cortical and subcortical regions. The large-scale neurobiological changes that occur with TBI have been in part investigated using resting state functional magnetic resonance imaging (fMRI) and graph theory-based analysis of functional connectivity. The neuroimaging studies in human subjects emphasize the promise of graph theory measures as functional TBI biomarkers. Understanding the neuronal circuit changes that give rise to distinct graph theory-based measures of network reorganization in TBI is an area we are interested in and have been working on for several years.