Imaging of the cerebrum

Neuroradiology back to the future: brain imaging

The beginning of neuroradiology can be traced to the early 1900s with the use of skull radiographs. Ventriculography and pneumoencephalography were introduced in 1918 and 1919, respectively, and carotid angiography, in 1927. Technical advances were made in these procedures during the next 40 years that lead to improved diagnosis of intracranial pathology. Yet, they remained invasive procedures that were often uncomfortable and associated with significant morbidity. The introduction of CT in 1971 revolutionized neuroradiology. Ventriculography and pneumoencephalography were rendered obsolete. The imaging revolution continued with the advent of MR imaging in the early 1980s. Noninvasive angiographic techniques have curtailed the use of conventional angiography, and physiologic imaging gives us a window into the function of the brain. In this historical review, we will trace the origin and evolution of the advances that have led to the quicker, less invasive diagnosis and resulted in more rapid therapy and improved outcomes.

Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution.