multimodal imaging

Why is 7T MRI indispensable in epilepsy now?

Identifying a structural brain lesion on MRI is the most important factor that correlates with seizure freedom after surgery in patients suffering from drug-resistant focal epilepsy. By providing better image contrast and higher spatial resolution, structural MRI at 7 Tesla (7T) can lead to lesion detection in about 25% of patients presenting with negative MRI at lower fields. In addition to a better detection/delineation/phenotyping of epileptogenic lesions, higher signal at ultra-high field also facilitates more detailed analyses of several functional and molecular alterations of tissues, susceptible to detect epileptogenic properties even in absence of visible lesions. These advantages but also the technical challenges of 7T MRI in practice will be presented and discussed.

Multimodal imaging in Dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a synucleinopathy but more than half of patients with DLB also have varying degrees of tau and amyloid-β co-pathology. Identifying and tracking the pathologic heterogeneity of DLB with multi-modal biomarkers is critical for the design of clinical trials that target each pathology early in the disease at a time when prevention or delaying the transition to dementia is possible. Furthermore, longitudinal evaluation of multi-modal biomarkers contributes to our understanding of the type and extent of the pathologic progression and serves to characterize the temporal emergence of the associated phenotypic expression. This talk will focus on the utility of multi-modal imaging in DLB.

From 1D to 5D: Data-driven Discovery of Whole-brain Dynamic Connectivity in fMRI Data

The analysis of functional magnetic resonance imaging (fMRI) data can greatly benefit from flexible analytic approaches. In particular, the advent of data-driven approaches to identify whole-brain time-varying connectivity and activity has revealed a number of interesting relevant variation in the data which, when ignored, can provide misleading information. In this lecture I will provide a comparative introduction of a range of data-driven approaches to estimating time-varying connectivity. I will also present detailed examples where studies of both brain health and disorder have been advanced by approaches designed to capture and estimate time-varying information in resting fMRI data. I will review several exemplar data sets analyzed in different ways to demonstrate the complementarity as well as trade-offs of various modeling approaches to answer questions about brain function. Finally, I will review and provide examples of strategies for validating time-varying connectivity including simulations, multimodal imaging, and comparative prediction within clinical populations, among others. As part of the interactive aspect I will provide a hands-on guide to the dynamic functional network connectivity toolbox within the GIFT software, including an online didactic analytic decision tree to introduce the various concepts and decisions that need to be made when using such tools

Neural correlates of affective empathy in aging: A multimodal imaging, multivariate approach

Sample preparation and warping accuracy for correlative multimodal imaging in the mouse olfactory bulb using 2-photon, synchrotron X-ray and volume electron microscopy