Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning.

BACKGROUND: The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established. METHODS: The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods. RESULTS: Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity. CONCLUSIONS: Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Functional magnetic resonance microscopy at single-cell resolution in Aplysia californica.

In this work, we show the feasibility of performing functional MRI studies with single-cell resolution. At ultrahigh magnetic field, manganese-enhanced magnetic resonance microscopy allows the identification of most motor neurons in the buccal network of Aplysia at low, nontoxic Mn(2+) concentrations. We establish that Mn(2+) accumulates intracellularly on injection into the living Aplysia and that its concentration increases when the animals are presented with a sensory stimulus. We also show that we can distinguish between neuronal activities elicited by different types of stimuli. This method opens up a new avenue into probing the functional organization and plasticity of neuronal networks involved in goal-directed behaviors with single-cell resolution.

Effects of hypotonic stress and ouabain on the apparent diffusion coefficient of water at cellular and tissue levels in Aplysia.

There is evidence that physiological or pathological cell swelling is associated with a decrease of the apparent diffusion coefficient (ADC) of water in tissues, as measured with MRI. However the mechanism remains unclear. Magnetic resonance microscopy, performed on small tissue samples, has the potential to distinguish effects occurring at cellular and tissue levels. A three-dimensional diffusion prepared fast imaging with steady-state free precession sequence for MR microscopy was implemented on a 17.2 T imaging system and used to investigate the effect of two biological challenges known to cause cell swelling, exposure to a hypotonic solution or to ouabain, on Aplysia nervous tissue. The ADC was measured inside isolated neuronal soma and in the region of cell bodies of the buccal ganglia. Both challenges resulted in an ADC increase inside isolated neuronal soma (+31 ± 24% and +30 ± 11%, respectively) and an ADC decrease at tissue level in the buccal ganglia (-12 ± 5% and -18 ± 8%, respectively). A scenario involving a layer of water molecules bound to the inflating cell membrane surface is proposed to reconcile this apparent discrepancy.