In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

Anatomo-functional correspondence in the superior temporal sulcus

The superior temporal sulcus (STS) is an intriguing region both for its complex anatomy and for the multiple functions that it hosts. Unfortunately, most studies explored either the functional organization or the anatomy of the STS only. Here, we link these two aspects by investigating anatomo-functional correspondences between the voice-sensitive cortex (Temporal Voice Areas) and the STS depth. To do so, anatomical and functional scans of 116 subjects were processed such as to generate individual surface maps on which both depth and functional voice activity can be analyzed. Individual depth profiles of manually drawn STS and functional profiles from a voice localizer (voice > non-voice) maps were extracted and compared to assess anatomo-functional correspondences. Three major results were obtained: first, the STS exhibits a highly significant rightward depth asymmetry in its middle part. Second, there is an anatomo-functional correspondence between the location of the voice-sensitive peak and the deepest point inside this asymmetrical region bilaterally. Finally, we showed that this correspondence was independent of the gender and, using a machine learning approach, that it existed at the individual level. These findings offer new perspectives for the understanding of anatomo-functional correspondences in this complex cortical region

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Functional MRI investigations of path integration and goal-directed navigation in humans

Path integration is a navigational process that humans and animals use to track changes in their position and orientation. Animal and computational studies suggest that a spatially-tuned navigation system supports path integration, yet this system is not well understood in humans. Here, the prediction was tested that path integration mechanisms and goal-directed navigation in humans would recruit the same key brain regions within the parietal cortex and medial temporal lobes as predicted by animal and computational models. The three experiments described in this dissertation used behavioral and functional magnetic resonance imaging methods in 131 adults (18-35 years) to examine behavioral and brain correlates of navigation. In a landmark-free environment, path integration mechanisms are utilized to update position and orientation to a goal. Experiment 1 examined neural correlates of these mechanisms in the human brain. The results demonstrated that successful first and third person perspective navigation recruited the anterior hippocampus. The posterior hippocampus was found to track distance and temporal proximity to a goal location. The retrosplenial and posterior parietal cortices were additionally recruited for successful goal-directed navigation. In a landmark-rich environment, humans utilize route-based strategies to triangulate between their position, landmarks, and navigational goal. Experiment 2 contrasted path integration and landmark-based strategies by adding a solitary landmark to a sparse environment. The results demonstrated that successful navigation with and without an orienting landmark recruited the anterior hippocampus. Activity in the bilateral posterior hippocampus was modulated by larger triangulation between current position, landmark, and goal location during first person perspective navigation. The caudate nucleus was additionally recruited for landmark-based navigation. Experiment 3 used functional connectivity methods coupled with two fMRI tasks to determine whether areas responsive to optic flow, specifically V3A, V6, and the human motion complex (hMT+), are functionally connected to brain regions recruited during first person perspective navigation. The results demonstrated a functional relationship between optic flow areas and navigationally responsive regions, including the hippocampus, retrosplenial, posterior parietal, and medial prefrontal cortices. These studies demonstrate that goal-directed navigation is reliant upon a navigational system supported by hippocampal position computations and orientation calculations from the retrosplenial and posterior parietal cortices

Landmarking the brain for geometric morphometric analysis: An error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes. © 2014 Chollet et al

Subjectivity and complexity of facial attractiveness

The origin and meaning of facial beauty represent a longstanding puzzle. Despite the profuse literature devoted to facial attractiveness, its very nature, its determinants and the nature of inter-person differences remain controversial issues. Here we tackle such questions proposing a novel experimental approach in which human subjects, instead of rating natural faces, are allowed to efficiently explore the face-space and 'sculpt' their favorite variation of a reference facial image. The results reveal that different subjects prefer distinguishable regions of the face-space, highlighting the essential subjectivity of the phenomenon.The different sculpted facial vectors exhibit strong correlations among pairs of facial distances, characterising the underlying universality and complexity of the cognitive processes, and the relative relevance and robustness of the different facial distances.Comment: 15 pages, 5 figures. Supplementary information: 26 pages, 13 figure

The Role of Landscapes and Landmarks in Bee Navigation: A Review.

The ability of animals to explore landmarks in their environment is essential to their fitness. Landmarks are widely recognized to play a key role in navigation by providing information in multiple sensory modalities. However, what is a landmark? We propose that animals use a hierarchy of information based upon its utility and salience when an animal is in a given motivational state. Focusing on honeybees, we suggest that foragers choose landmarks based upon their relative uniqueness, conspicuousness, stability, and context. We also propose that it is useful to distinguish between landmarks that provide sensory input that changes ("near") or does not change ("far") as the receiver uses these landmarks to navigate. However, we recognize that this distinction occurs on a continuum and is not a clear-cut dichotomy. We review the rich literature on landmarks, focusing on recent studies that have illuminated our understanding of the kinds of information that bees use, how they use it, potential mechanisms, and future research directions

Fusion and visualization of intraoperative cortical images with preoperative models for epilepsy surgical planning and guidance.

OBJECTIVE: During epilepsy surgery it is important for the surgeon to correlate the preoperative cortical morphology (from preoperative images) with the intraoperative environment. Augmented Reality (AR) provides a solution for combining the real environment with virtual models. However, AR usually requires the use of specialized displays, and its effectiveness in the surgery still needs to be evaluated. The objective of this research was to develop an alternative approach to provide enhanced visualization by fusing a direct (photographic) view of the surgical field with the 3D patient model during image guided epilepsy surgery. MATERIALS AND METHODS: We correlated the preoperative plan with the intraoperative surgical scene, first by a manual landmark-based registration and then by an intensity-based perspective 3D-2D registration for camera pose estimation. The 2D photographic image was then texture-mapped onto the 3D preoperative model using the solved camera pose. In the proposed method, we employ direct volume rendering to obtain a perspective view of the brain image using GPU-accelerated ray-casting. The algorithm was validated by a phantom study and also in the clinical environment with a neuronavigation system. RESULTS: In the phantom experiment, the 3D Mean Registration Error (MRE) was 2.43 ± 0.32 mm with a success rate of 100%. In the clinical experiment, the 3D MRE was 5.15 ± 0.49 mm with 2D in-plane error of 3.30 ± 1.41 mm. A clinical application of our fusion method for enhanced and augmented visualization for integrated image and functional guidance during neurosurgery is also presented. CONCLUSIONS: This paper presents an alternative approach to a sophisticated AR environment for assisting in epilepsy surgery, whereby a real intraoperative scene is mapped onto the surface model of the brain. In contrast to the AR approach, this method needs no specialized display equipment. Moreover, it requires minimal changes to existing systems and workflow, and is therefore well suited to the OR environment. In the phantom and in vivo clinical experiments, we demonstrate that the fusion method can achieve a level of accuracy sufficient for the requirements of epilepsy surgery

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Brain dynamic during landmark-based learning spatial navigation

In the current study, I investigated both human behavior and brain dynamics during spatial navigation to gain a better understanding of human navigational strategies and brain signals that underlie spatial cognition. To this end, a custom-built virtual reality task and a 64-channel scalp electroencephalogram (EEG) were utilized to study participants. At the first step, we presented a novel, straightforward, yet powerful tool to evaluate individual differences during navigation, comprising of a virtual radial-arm maze inspired to the animal experiments. The virtual maze is designed and furnished, similar to an art gallery, to provide a more realistic and exciting environment for subjects’ exploration. We investigated whether a different set of instructions (explicit or implicit) affects subjects’ navigational performance, and we assessed the effect of the set of instructions on exploration strategies during both place learning and recall. We tested 42 subjects and evaluated their way-finding ability. Individual differences were assessed through the analysis of the navigational paths, which permitted the isolation and definition of a few strategies adopted by both subjects who adopted a more explicit strategy, based on explicit instructions, and an implicit strategy, based on implicit instructions. The second step aimed to explore brain dynamics and neurophysiological activity during spatial navigation. More specifically, we aimed to figure out how navigational related brain regions are connected and how their interactions and electrical activity vary according to different navigational tasks and environment. This experiment was divided into two steps: learning phase and test phase. The same virtual maze (art gallery) as the behavioral part of the study was used so that subjects to perform landmark-based navigation. The main task of the experiment was finding and memorizing the position of some goals within the environment during the learning phase and retrieving the spatial information of the goals during the test phase. We recorded EEG signals of 20 subjects during the experiment, and both scalp-level and source-level analysis approaches were employed to figure out how the brain represents the spatial location of landmarks and targets and, more precisely, how different brain regions contribute to spatial orientation and landmark-based learning during navigation