Poster No:
225
Submission Type:
Abstract Submission
Authors:
Julian Beckmann1, Nika Ghavamizadeh2, David Salat3, Tatiana Sitnikova1
Institutions:
1Harvard Medical School / Mass General Hospital, Boston, MA, 2Mass General Hospital, Boston, MA, 3Harvard Medical School / Mass General Hospital / Boston VA, Boston, MA
First Author:
Co-Author(s):
David Salat, PhD
Harvard Medical School / Mass General Hospital / Boston VA
Boston, MA
Introduction:
Neurovascular coupling provides metabolic support to active brain tissue and is critical to brain health. Research in animal models of Alzheimer's disease (AD) showed that disruptions in neurovascular coupling cause brain deposition of beta-amyloid (Aβ), as hypoxia increases production of the pathological form (Aβ42) of this protein and loss of vasomotion reduces protein clearance. Aβ accumulation in the brain is a hallmark of Alzheimer's pathology. We examined if disruptions in neurovascular coupling in the human brain are linked to Aβ pathology in older adults who may be in the early preclinical stages of AD.
Methods:
Study participants were adults between 60 and 80 years of age who had not been diagnosed with dementia. Neurovascular coupling was measured through the simultaneous acquisition of electroencephalographic (EEG) and functional magnetic resonance (fMRI) data during a resting brain scan. EEG was collected with a high-density sensor array (Brain Vision Inc.) and was source-localized to the cerebral cortex using beamforming. The observed electrophysiological brain activity was segmented into discrete subnetwork activation states using hidden Markov modeling. Blood oxygen level-dependent (BOLD) signal was recorded with fast-acquisition fMRI (multi-slice, 3T Siemens). Neurovascular coupling was quantified as the rate of change in the BOLD signal evoked by variable-duration electrophysiological network activations. The hemodynamic response was estimated by a finite impulse response (FIR) model. General vascular brain pathology was indexed by white matter signal abnormalities (WMSA), detected in structural T1 magnetic resonance images, and believed to be a consequence of cerebrovascular dysfunction. Aβ brain pathology was estimated by blood biomarkers (Quanterix) that have been shown to correlate with Aβ deposition in the brain, as documented by positron emission tomography (PET). The study design was cross-sectional.
Results:
The hidden Markov modeling detected short-lived electrophysiological activity states in default mode (DMN), attention, visual, sensorimotor, and other cortical networks. These activations varied in duration (50-500ms-long) and came in an irregular, jittered succession. There was a readily apparent similarity in the cerebral topography between the electrophysiological states and the BOLD changes evoked by these states. Neurovascular coupling in the DMN, measured as the rate of BOLD signal change granted the duration differences in the eliciting electrophysiological activations, was correlated with blood biomarkers. Across our sample of older adults, a reduced range of neurovascular coupling in the DMN predicted elevated Aβ pathology levels as quantified by blood plasma Aβ42/Aβ40 ratio and p-tau 181. In contrast, general vascular pathology, as measured by WMSA, was not associated with blood biomarkers in our study sample. An association between the DMN neurovascular coupling and WMSA was weak.
Conclusions:
Effective quantification of neurovascular coupling may be possible by simultaneously acquiring EEG, which measures electrophysiological neural activity, and fMRI, which tracks cerebrovascular fluctuations. Neurovascular coupling quantified in specific neural networks in the human brain can be a sensitive metric enabling the study of vasculogenic brain pathology. We show that neurovascular coupling in DMN could be linked to early AD pathology -- before neuroimaging markers of general vascular pathology, such as WMSA, become associated with Aβ pathology, as was found in prior studies of AD. The DMN has been previously reported to accumulate Aβ early in the AD progression. Our results are consistent with a hypothesis that disruptions of neurovascular coupling in this network may presage the mishandling of Aβ in the brain in older adults during the early AD stages. Future work is warranted to gain better insight into a longitudinal relationship between vascular and protein changes in early AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
EEG
Keywords:
Aging
Cerebral Blood Flow
Data analysis
Electroencephaolography (EEG)
FUNCTIONAL MRI
Machine Learning
Source Localization
Other - blood biomarkers
1|2Indicates the priority used for review
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