Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links through Daily Activity Patterns and Brain Connectivity Analysis

BACKGROUND

Better understanding apathy in late-life depression (LLD) would help predicting poor prognosis of the disease such as dementia. Actimetry provides an objective and ecological measure of apathy from patients' daily motor activity. We aimed to determine if patterns of motor activity were associated with apathy and brain connectivity in networks underlying goal-directed behaviors.

METHODS

Resting-state functional MRI and diffusion MRI were collected from 38 non-demented LLD subjects. Apathy was evaluated using the diagnostic criteria for apathy, the apathy evaluation scale (AES) and the apathy motivation index (AMI). Functional principal components (fPC) of motor activity were derived from actimetry recordings of 72 hours. Associations between fPC and apathy were estimated by linear regression. Subnetworks whose connectivity was significantly associated with fPC were identified via the threshold-free network-based statistics. The relationship between apathy and microstructure metrics was estimated along fibers by diffusion tensor imaging and a multicompartment model called neurite orientation dispersion and density imaging via tractometry.

RESULTS

We found two fPC associated with apathy: mean diurnal activity, negatively associated with AES, and an early chronotype, negatively associated with AMI. Mean diurnal activity was associated with increased connectivity in the default-mode, the cingulo-opercular and the frontoparietal networks, while chronotype was associated with a more heterogenous connectivity pattern in the same networks. We did not find significant associations between microstructural metrics and fPCs.

CONCLUSION

Our findings suggest that mean diurnal activity and chronotype could provide indirect ambulatory measures of apathy in LLD, associated with modified functional connectivity of brain networks underlying goal-directed behaviors.

Imaging white matter microstructure with gradient-echo phase imaging: Is ex vivo imaging with formalin-fixed tissue a good approximation of the in vivo brain?

Purpose

Ex vivo imaging is a commonly used approach to investigate the biophysical mechanism of orientation‐dependent signal phase evolution in white matter. Yet, how phase measurements are influenced by the structural alteration in the tissue after formalin fixation is not fully understood. Here, we study the effects on magnetic susceptibility, microstructural compartmentalization, and chemical exchange measurement with a postmortem formalin‐fixed whole‐brain human tissue.

Methods

A formalin‐fixed, postmortem human brain specimen was scanned with multiple orientations to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. White matter samples were subsequently excised from the whole‐brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure‐related contributions in the signal phase and to validate the findings of the whole‐brain data.

Results

The bulk isotropic magnetic susceptibility of ex vivo whole‐brain imaging is comparable to in vivo imaging, with noticeable enhanced nonsusceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalization phase effect were considerably reduced in the formalin‐fixed white matter specimens.

Conclusions

Formalin‐fixed postmortem white matter exhibits comparable isotropic magnetic susceptibility to previous in vivo imaging findings. However, the measured phase and magnitude data of the fixed white matter tissue shows a significantly weaker orientation dependency and compartmentalization effect. Alternatives to formalin fixation are needed to better reproduce the in vivo microstructural effects in postmortem samples.

Decoding the microstructural properties of white matter using realistic models

Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some of the white matter (WM) signal behaviour of the hollow cylinder model, it has been shown that realistic models of WM offer a better description of the signal behaviour observed. In this work, we present a pipeline to (i) generate realistic 2D WM models with their microstructure based on real axon morphology with adjustable fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D WM models can be used to simulate a MR signal that provides a good approximation of the signal obtained from a real 3D WM model derived from electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if ME-GRE multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A deep learning network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex vivo dataset acquired in 9 orientations with respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics.