Novel Imaging Acquisition Methods

Neuroimaging research has traditionally approached data reliability through large-N consortium studies. However, in recent years, precision mapping studies, focusing on obtaining a high signal-to-noise ratio from a small group of individuals, have opened new avenues of exploring brain function [1]. Precision mapping takes a subject-specific approach to localize spatial and organizational variability in brain networks [2,3]. Although widely known in functional magnetic resonance imaging (fMRI) literature, this approach is yet to be established in optical imaging. In this study, we demonstrate the effectiveness of using High-Density Diffuse Optical Tomography (HD-DOT) to generate high-fidelity single-subject cortical maps [4]. HD-DOT is an optical imaging technique that uses dense, regularly spaced arrays of sources and detectors to obtain overlapping measurements of the underlying neuronal activity based on the absorption properties of hemoglobin in the blood [5-8]. We use an imaging system consisting of 128 sources and 125 detectors, with over 2500 measurements providing state-of-the-art HD-DOT image quality and extended cortical coverage (with a flatfield depth sensitivity >50% max, that extends to 20 mm beneath the scalp surface). Unlike fMRI, HD-DOT has a significant advantage of conducting scans while seated comfortably in a quiet and naturalistic environment. 

Key aspects of brain function can only be understood by recording from the entire brain in parallel, rather than parts of it in sequence. While fMRI has excellent full-brain coverage and spatial resolution, its temporal resolution is limited by the relatively slow hemodynamic response. And while electrophysiological approaches have excellent temporal resolution, they either 1) don't cover the entire brain (microscopic electrophysiology) or 2) have limited spatial resolution (macroscopic electrophysiology). 

Magnetoencephalography (MEG) is a non-invasive technique that measures the tiny magnetic fields generated by neural currents in the brain. Conventional MEG operates with superconducting SQUID magnetometers that must be immersed in liquid helium for cooling, which introduces a substantial gap between the sensors and the scalp. Optically-pumped magnetometers (OPMs) are new, highly sensitive magnetometers that operate without the need for cryogenic cooling and can be placed close to the scalp, substantially improving sensitivity to cortical sources (Boto et al., 2016, Iivanainen et al., 2017). The typical spatial resolution achieved by conventional MEG is not sufficient for laminar inference. One strategy to distinguish between deep and superficial sources is to use high-precision forward models that exploit the small variations in the so-called lead fields between deep and superficial sources (Bonaiuto et al., 2018a and 2018b). On-scalp OPM-MEG has been suggested to further improve the discriminability of laminar sources. To investigate this, we simulate cortical sources at deep and superficial layers and infer their laminar origin using OPM sensor arrays with varying numbers of sensors and measurement axes. 

Quantitative MRI (qMRI) is highly valuable method to estimate the human brain microstructural changes during aging and disease. An important goal of qMRI field is to provide reliable multi-parametric brain maps¹, with T1 map being commonly used. Two main acquisition approaches to quantify T1 on clinical scanners are variable flip angle (VFA) and Magnetization Prepared with 2 Rapid Gradient Echoes (MP2RAGE), yet the agreement between these approaches was not determined. A potential benefit of VFA is that it also allows the extraction of proton density (PD) map², which is not often explicitly extracted using the MP2RAGE formalism or mentioned in the original publications^3,4. In the brain, normalized PD is used to estimate the water fraction (WF). In this work, we first examined the agreement between the T1 maps obtained from these two approaches. Second, we presented a pipeline to obtain PD and WF maps from the MP2RAGE protocol that agree well with the VFA's maps. Hence, this work obtains an additional qMRI map for the MP2RAGE approach which is in agreement with the VFA approach. 

During evolution, the human brain has continuously expanded and increased its energy demands relative to the body (1). The most expanded brain areas have been associated with higher cognitive functions in humans (2), increasing considerably the energy demands related to neural processing (3). Patients with mental disorders often exhibit impaired cognitive functions (4). We hypothesized that the distribution of energy metabolism along signaling pathways could reveal mechanisms of higher cognitive processing in the human brain and its deviation in disease. 

We present the Connectome 2.0 scanner[1], developed for next-generation human connectomics and microstructure imaging of human brain circuits across scales. Here, we report on the hardware's design, construction, and evaluation, and initial results for in vivo human brain diffusion MRI.