The trigeminal system: The meningovascular complex- A review.

Supratentorial sensory perception, including pain, is subserved by the trigeminal nerve, in particular, by the branches of its ophthalmic division, which provide an extensive innervation of the dura mater and of the major brain blood vessels. In addition, contrary to previous assumptions, studies on awake patients during surgery have demonstrated that the mechanical stimulation of the pia mater and small cerebral vessels can also produce pain. The trigeminovascular system, located at the interface between the nervous and vascular systems, is therefore perfectly positioned to detect sensory inputs and influence blood flow regulation. Despite the fact that it remains only partially understood, the trigeminovascular system is most probably involved in several pathologies, including very frequent ones such as migraine, or other severe conditions, such as subarachnoid haemorrhage. The incomplete knowledge about the exact roles of the trigeminal system in headache, blood flow regulation, blood barrier permeability and trigemino-cardiac reflex warrants for an increased investigation of the anatomy and physiology of the trigeminal system. This translational review aims at presenting comprehensive information about the dural and brain afferents of the trigeminovascular system, in order to improve the understanding of trigeminal cranial sensory perception and to spark a new field of exploration for headache and other brain diseases.

Heavy Prenatal Alcohol Exposure is Related to Smaller Corpus Callosum in Newborn MRI Scans.

BACKGROUND: Magnetic resonance imaging (MRI) studies have consistently demonstrated disproportionately smaller corpus callosa in individuals with a history of prenatal alcohol exposure (PAE) but have not previously examined the feasibility of detecting this effect in infants. Tissue segmentation of the newborn brain is challenging because analysis techniques developed for the adult brain are not directly transferable, and segmentation for cerebral morphometry is difficult in neonates, due to the latter's incomplete myelination. This study is the first to use volumetric structural MRI to investigate PAE effects in newborns using manual tracing and to examine the cross-sectional area of the corpus callosum (CC). METHODS: Forty-three nonsedated infants born to 32 Cape Coloured heavy drinkers and 11 controls recruited prospectively during pregnancy were scanned using a custom-designed birdcage coil for infants, which increases signal-to-noise ratio almost 2-fold compared to the standard head coil. Alcohol use was ascertained prospectively during pregnancy, and fetal alcohol spectrum disorders diagnosis was conducted by expert dysmorphologists. Data were acquired using a multi-echo FLASH protocol adapted for newborns, and a knowledge-based procedure was used to hand-segment the neonatal brains. RESULTS: CC was disproportionately smaller in alcohol-exposed neonates than controls after controlling for intracranial volume. By contrast, CC area was unrelated to infant sex, gestational age, age at scan, or maternal smoking, marijuana, or methamphetamine use during pregnancy. CONCLUSIONS: Given that midline craniofacial anomalies have been recognized as a hallmark of fetal alcohol syndrome in humans and animal models since this syndrome was first identified, the CC deficit identified here in newborns may support early identification of a range of midline structural impairments. Smaller CC during the newborn period may provide an early indicator of fetal alcohol-related cognitive deficits that have been linked to this critically important brain structure in childhood and adolescence.

Feature-based alignment of volumetric multi-modal images.

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology.