https://www.nmr.mgh.harvard.edu/~iglesias/pdf/Media2015surveyFusion.pdf

Multi-Atlas Segmentation of Biomedical Images: A Survey

Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI) decoding is currently attracting increasing attention, due to accumulating documentation of its simplicity, accuracy, robustness and transfer learning capabilities, including the winning score obtained in five recent international predictive modeling BCI data competitions. The Riemannian framework is sharp from a mathematical perspective, yet in practice it is simple, both algorithmically and computationally. This allows the conception of online decoding machines suiting real-world operation in adverse conditions. We provide here a review on the use of Riemannian geometry for BCI and a primer on the classification frameworks based on it. While the theoretical research on Riemannian geometry is technical, our aim here is to show the appeal of the framework on an intuitive geometrical ground. In particular, we provide a rationale for its robustness and transfer learning capabilities and we elucidate the link between a simple Riemannian classifier and a state-of-the-art spatial filtering approach. We conclude by reporting details on the construction of data points to be manipulated in the Riemannian framework in the context of BCI and by providing links to available open-source Matlab and Python code libraries for designing BCI decoders.

/pdf/riemannian-geometry-for-eeg-based-brain-computer-interfaces-4421gmmsf7.pdf

Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review

Tau is a hallmark pathology of Alzheimer's disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β-amyloid (Aβ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer's disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain Aβ is present, but regions with greater Aβ burden show greater tau than predicted by connectivity patterns, suggesting a role of Aβ in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain Aβ.

/pdf/spread-of-pathological-tau-proteins-through-communicating-bsvdk2sbmz.pdf

Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 - 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

pdf/multi-atlas-segmentation-of-biomedical-images-a-survey-1bpfe0t35g.pdf

The central complex is a group of modular neuropils across the midline of the insect brain. Hallmarks of its anatomical organization are discrete layers, an organization into arrays of 16 slices along the right-left axis, and precise inter-hemispheric connections via chiasmata. The central complex is connected most prominently with the adjacent lateral complex and the superior protocerebrum. Its developmental appearance corresponds with the appearance of compound eyes and walking legs. Distinct dopaminergic neurons control various forms of arousal. Electrophysiological studies provide evidence for roles in polarized light vision, sky compass orientation, and integration of spatial information for locomotor control. Behavioral studies on mutant and transgenic flies indicate roles in spatial representation of visual cues, spatial visual memory, directional control of walking and flight, and place learning. The data suggest that spatial azimuthal directions (i.e., where) are represented in the slices, and cu...

Organization and Functional Roles of the Central Complex in the Insect Brain

Animals must routinely deal with barriers as they move through their natural environment. These challenges require directed changes in leg movements and posture performed in the context of ever changing internal and external conditions. In particular, cockroaches use a combination of tactile and visual information to evaluate objects in their path in order to effectively guide their movements in complex terrain. When encountering a large block, the insect uses its antennae to evaluate the object’s height then rears upward accordingly before climbing. A shelf presents a choice between climbing and tunneling that depends on how the antennae strike the shelf; tapping from above yields climbing, while tapping from below causes tunneling. However, ambient light conditions detected by the ocelli can bias that decision. Similarly, in a T-maze turning is determined by antennal contact but influenced by visual cues. These multi-sensory behaviors led us to look at the central complex as a center for sensori-motor integration within the insect brain. Visual and antennal tactile cues are processed within the central complex and, in tethered preparations, several central complex units changed firing rates in tandem with or prior to altered step frequency or turning, while stimulation through the implanted electrodes evoked these same behavioral changes. To further test for a central complex role in these decisions, we examined behavioral effects of brain lesions. Electrolytic lesions in restricted regions of the central complex generated site specific behavioral deficits. Similar changes were also found in reversible effects of procaine injections in the brain. Finally, we are examining these kinds of decisions made in a large arena that more closely matches the conditions under which cockroaches forage. Overall, our studies suggest that CC circuits may indeed influence the descending commands associated with navigational decisions, thereby making them more context dependent.

/pdf/deciding-which-way-to-go-how-do-insects-alter-movements-to-35ccec33sc.pdf

Deciding Which Way to Go: How Do Insects Alter Movements to Negotiate Barriers?

A neuromechanical simulation of the cockroach Blaberus discoidalis was developed to explore changes in locomotion when the animal transitions from walking straight to turning. The simulation was based upon the biological data taken from three sources. Neural circuitry was adapted from the extensive literature primarily obtained from the studies of neural connections within thoracic ganglia of stick insect and adapted to cockroach. The 3D joint kinematic data on straight, forward walking for cockroach were taken from a paper that describes these movements in all joints simultaneously as the cockroach walked on an oiled-plate tether (Bender et al. in PloS one 5(10):1---15, 2010b). Joint kinematics for turning were only available for some leg joints (Mu and Ritzmann in J Comp Physiol A Neuroethol Sens Neural Behav Physiol 191(11):1037---54, 2005) and thus had to be obtained using the methods that were applied for straight walking by Bender et al. (PloS one 5(10):1---15, 2010b). Once walking, inside turning, and outside turning were characterized, phase and amplitude changes for each joint of each leg were quantified. Apparent reflex reversals and joint activity changes were used to modify sensory coupling pathways between the CPG at each joint of the simulation. Oiled-plate experiments in simulation produced tarsus trajectories in stance similar to those seen in the animal. Simulations including forces that would be experienced if the insect was walking freely (i.e., weight support and friction) again produced similar results. These data were not considered during the design of the simulation, suggesting that the simulation captures some key underlying the principles of walking, turning, and transitioning in the cockroach. In addition, since the nervous system was modeled with realistic neuron models, biologically plausible reflex reversals are simulated, motivating future neurobiological research.

A neuromechanical simulation of insect walking and transition to turning of the cockroach Blaberus discoidalis

............................................................................................................................ 5 INTRODUCTION ........................................................................................................... 6 INSECT LOCOMOTION ............................................................................................. 6 LEG SEGMENTS AND JOINTS ................................................................................... 7 JOINT CONTROL RULES ......................................................................................... 10 NEURAL CONTROL ................................................................................................ 14 Head Ganglia ............................................................................................... 14 Thoracic Ganglia .......................................................................................... 16 CENTRAL PATTERN GENERATORS ........................................................................ 17 JOINT COORDINATION ........................................................................................... 18 SENSORY STRUCTURES AND REFLEXES ................................................................ 19 External Receptors ......................................................................................... 19 Receptors Associated with Legs ........................................................................ 20 REFLEX MODEL OF LEG CONTROL ........................................................................ 24 INTER-LEG CONTROL ............................................................................................ 25 COCKROACH TURNING ......................................................................................... 27 PURPOSE OF PROJECT ............................................................................................ 29 METHODS ..................................................................................................................... 30 RESULTS ....................................................................................................................... 39 DISCUSSION ................................................................................................................. 59 CONCLUSION .............................................................................................................. 68 APPENDIX ..................................................................................................................... 69 References ....................................................................................................................... 81

Multi-legged Joint Kinematic Analysis of an Insect Tethered over a Slippery Surface

The American Board of Orthopaedic Surgery requirements state that an orthopaedic residency must offer at least 5 years of clinical education and some exposure to research. To expose residents to basic research, some programs, including ours, have a research track that allows for 1 year of basic science research. The degree to which research productivity during residency—which may be something that can perhaps be influenced by interventions like research tracks during residency—affects residency graduates’ future research contributions is unknown. Our research goals were to determine whether (1) residents who published in a peer-reviewed journal during residency were more likely to publish in their careers after graduation; (2) residents who participated in an elective research year were more likely to publish at least one paper in a peer-reviewed journal during residency; and (3) residents who participated in the research year were more likely to choose academic careers. Using questionnaires, online PubMed searches, and office contact, the career paths (academic versus private practice) and publications in peer-reviewed journals of all 122 Case Western Reserve University orthopaedics residents who completed training from 1987 to 2006 were analyzed. Seventy-five percent of residents who published peer-reviewed research during residency continued with peer-reviewed publications in their careers versus 55% of residents who did not publish during residency (p = 0.02). No difference in career paths was observed between the Case Western Reserve University research and traditional track-trained surgeons. During residency, however, research track-trained surgeons were more likely to publish in peer-reviewed journals (71% versus 41% of traditional track-trained surgeons, p < 0.01). Residents who publish in a peer-reviewed journal during residency are more likely to continue publishing in their future careers as orthopaedic surgeons. Future studies are needed to elucidate the causative factors in the association between publishing in a peer-reviewed journal during training and further contributions later in an orthopaedic surgeon’s career.

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Deciding Which Way to Go: How Do Insects Alter Movements to Negotiate Barriers?

A neuromechanical simulation of insect walking and transition to turning of the cockroach Blaberus discoidalis

Multi-legged Joint Kinematic Analysis of an Insect Tethered over a Slippery Surface

Does Research Participation Make a Difference in Residency Training