Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

Increased NLRP1 mRNA and Protein Expression Suggests Inflammasome Activation in the Dorsolateral Prefrontal and Medial Orbitofrontal Cortex in Schizophrenia.

Schizophrenia is a complex mental condition, with key symptoms marked for diagnosis including delusions, hallucinations, disorganized thinking, reduced emotional expression, and social dysfunction. In the context of major developmental hypotheses of schizophrenia, notably those concerning maternal immune activation and neuroinflammation, we studied NLRP1 expression and content in the postmortem brain tissue of 10 schizophrenia and 10 control subjects. In the medial orbitofrontal cortex (Brodmann's area 11/12) and dorsolateral prefrontal cortex (area 46) from both hemispheres of six schizophrenia subjects, the NLRP1 mRNA expression was significantly higher than in six control brains (p < 0.05). As the expression difference was highest for the medial orbitofrontal cortex in the right hemisphere, we assessed NLRP1-immunoreactive pyramidal neurons in layers III, V, and VI in the medial orbitofrontal cortex in the right hemisphere of seven schizophrenia and five control brains. Compared to controls, we quantified a significantly higher number of NLRP1-positive pyramidal neurons in the schizophrenia brains (p < 0.01), suggesting NLRP1 inflammasome activation in schizophrenia subjects. Layer III pyramidal neuron dysfunction aligns with working memory deficits, while impairments of pyramidal neurons in layers V and VI likely disrupt predictive processing. We propose NLRP1 inflammasome as a potential biomarker and therapeutic target in schizophrenia.

Fetal development of the human amygdala.

The intricate development of the human amygdala involves a complex interplay of diverse processes, varying in speed and duration. In humans, transient cytoarchitectural structures deliquesce, leading to the formation of functionally distinct nuclei as a result of multiple interdependent developmental events. This study compares the amygdala's cytoarchitectural development in conjunction with specific antibody reactivity for neuronal, glial, neuropil, and radial glial fibers, synaptic, extracellular matrix, and myelin components in 39 fetal human brains. We recognized that the early fetal period, as a continuation of the embryonic period, is still dominated by relatively uniform histogenetic processes. The typical appearance of ovoid cell clusters in the lateral nucleus during midfetal period is most likely associated with the cell migration and axonal growth processes in the developing human brain. Notably, synaptic markers are firstly detected in the corticomedial group of nuclei, while immunoreactivity for the panaxonal neurofilament marker SMI 312 is found dorsally. The late fetal period is characterized by a protracted migration process evidenced by the presence of doublecortin and SOX-2 immunoreactivity ventrally, in the prospective paralaminar nucleus, reinforced by vimentin immunoreactivity in the last remaining radial glial fibers. Nearing the term period, SMI 99 immunoreactivity indicates that perinatal myelination becomes prominent primarily along major axonal pathways, laying the foundation for more pronounced functional maturation. This study comprehensively elucidates the rate and sequence of maturational events in the amygdala, highlighting the key role of prenatal development in its behavioral, autonomic, and endocrine regulation, with subsequent implications for both normal functioning and psychiatric disorders.