Toward Objective Quantification of Perfusion-weighted Computed Tomography in Subarachnoid Hemorrhage: Quantification of Symmetry and Automated Delineation of Vascular Territories

Rationale and Objectives: Perfusion-weighted computed tomography (CTP) is a relatively recent innovation that estimates a value for cerebral blood flow (CBF) using a series of axial head CT images tracking the time course of a signal from an intravenous contrast bolus. Materials and Methods: CTP images were obtained using a standard imaging protocol and were analyzed using commercially available software. A novel computer-based method was used for objective quantification of side-to-side asymmetries of CBF values calculated from CTP images. Results: Our method corrects for the inherent variability of the CTP methodology seen in the subarachnoid hemorrhage (SAH) patient population to potentially aid in the diagnosis of cerebral vasospasm (CVS). This method analyzes and quantifies side-to-side asymmetry of CBF and presents relative differences in a construct termed a Relative Difference Map (RDM). To further automate this process, we have developed a unique methodology that enables a computer to delineate vascular territories within a brain image, regardless of the size and shape of the brain. Conclusions: While both the quantification of image symmetry using RDMs and the automated assignment of vascular territories were initially designed for the analysis of CTP images, it is likely that they will be useful in a variety of applications

Irregular S-cone mosaics in felid retinas: spatial interaction with axonless horizontal revealed by cross-correlation

In most mammals short-wavelength-sensitive (S) cones are arranged in irregular patterns with widely variable intercell distances. Consequently, mosaics of connected interneurons either may show some type of correlation to photoreceptor placement or may establish an independent lattice with compensatory dendritic organization. Since axonless horizontal cells (A-HC’s) are supposed to direct all dendrites to overlying cones, we studied their spatial interaction with chromatic cone subclasses. In the cheetah, the bobcat, and the leopard, anti-S-opsin antibodies have consistently colabeled the A-HC’s in addition to the S cones. We investigated the interaction between the two cell mosaics, using autocorrelation and cross-correlation procedures, including a Voronoi-based density probe. Comparisons with simulations of random mosaics show significantly lower densities of S cones above the cell bodies and primary dendrites of A-HC’s. The pattern results in different long-wavelength-sensitive-L- and S-cone ratios in the central versus the peripheral zones of A-HC dendritic fields. The existence of a related pattern at the synaptic level and its potential significance for color processing may be investigated in further studies

Classification and Geometry of General Perceptual Manifolds

Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location, and intensity) of the same perceptual object. Object recognition and discrimination requires classifying the manifolds in a manner that is insensitive to variability within a manifold. How neuronal systems give rise to invariant object classification and recognition is a fundamental problem in brain theory as well as in machine learning. Here we study the ability of a readout network to classify objects from their perceptual manifold representations. We develop a statistical mechanical theory for the linear classification of manifolds with arbitrary geometry revealing a remarkable relation to the mathematics of conic decomposition. Novel geometrical measures of manifold radius and manifold dimension are introduced which can explain the classification capacity for manifolds of various geometries. The general theory is demonstrated on a number of representative manifolds, including L2 ellipsoids prototypical of strictly convex manifolds, L1 balls representing polytopes consisting of finite sample points, and orientation manifolds which arise from neurons tuned to respond to a continuous angle variable, such as object orientation. The effects of label sparsity on the classification capacity of manifolds are elucidated, revealing a scaling relation between label sparsity and manifold radius. Theoretical predictions are corroborated by numerical simulations using recently developed algorithms to compute maximum margin solutions for manifold dichotomies. Our theory and its extensions provide a powerful and rich framework for applying statistical mechanics of linear classification to data arising from neuronal responses to object stimuli, as well as to artificial deep networks trained for object recognition tasks.Comment: 24 pages, 12 figures, Supplementary Material

Exact Classification with Two-Layered Perceptrons

We study the capabilities of two-layered perceptrons for classifying exactly a given subset. Both necessary and sufficient conditions are derived for subsets to be exactly classifiable with two-layered perceptrons that use the hard-limiting response function. The necessary conditions can be viewed as generalizations of the linear-separability condition of one-layered perceptrons and confirm the conjecture that the capabilities of two-layered perceptrons are more limited than those of three-layered perceptrons. The sufficient conditions show that the capabilities of two-layered perceptrons extend beyond the exact classification of convex subsets. Furthermore, we present an algorithmic approach to the problem of verifying the sufficiency condition for a given subset

Successor Feature Sets: Generalizing Successor Representations Across Policies

Successor-style representations have many advantages for reinforcement learning: for example, they can help an agent generalize from past experience to new goals, and they have been proposed as explanations of behavioral and neural data from human and animal learners. They also form a natural bridge between model-based and model-free RL methods: like the former they make predictions about future experiences, and like the latter they allow efficient prediction of total discounted rewards. However, successor-style representations are not optimized to generalize across policies: typically, we maintain a limited-length list of policies, and share information among them by representation learning or GPI. Successor-style representations also typically make no provision for gathering information or reasoning about latent variables. To address these limitations, we bring together ideas from predictive state representations, belief space value iteration, successor features, and convex analysis: we develop a new, general successor-style representation, together with a Bellman equation that connects multiple sources of information within this representation, including different latent states, policies, and reward functions. The new representation is highly expressive: for example, it lets us efficiently read off an optimal policy for a new reward function, or a policy that imitates a new demonstration. For this paper, we focus on exact computation of the new representation in small, known environments, since even this restricted setting offers plenty of interesting questions. Our implementation does not scale to large, unknown environments -- nor would we expect it to, since it generalizes POMDP value iteration, which is difficult to scale. However, we believe that future work will allow us to extend our ideas to approximate reasoning in large, unknown environments

Epileptisten kohtauksien automaattinen tunnistaminen kaksiulotteisessa EEG-piirreavaruudessa

Epileptinen kohtaus on neurologinen häiriötila, joka ilmenee aivojen epänormaalina sähköisenä toimintana. Joihinkin kohtauksiin liittyy ulkoisia merkkejä, kuten lihaskouristuksia. Kohtauksia, joihin ei liity selkeitä ulkoisia merkkejä, kutsutaan ei-konvulsiiviksi. Ne voidaan tunnistaa vain seuraamalla aivojen sähköistä toimintaa. Ei-konvulsiivisten kohtauksien on osoitettu olevan erityisen yleisiä tehohoitopotilailla - myös sellaisilla potilailla, joilla ei ole aiemmin ollut kohtauksia. Epileptinen kohtaus on pikaista interventiota vaativa vakava tila. Aivosähkökäyrällä (elektroenkefalografia, EEG) voidaan tutkia aivojen sähköistä toimintaa. Datan läpikäynti käsin on aikaavievää, joten tehohoitoon sopivalle, automaattiselle ja reaaliaikaiselle analyysimenetelmälle on suuri tarve. Tässä diplomityössä esitellään kolme menetelmää, jotka soveltuvat signaalipiirteiden evoluution seuraamiseen. Kultakin EEG-kanavalta määritetään kaksi piirrettä: hetkellinen taajuus ja signaalin teho. Ensimmäinen menetelmä mittaa piirreavaruuteen muodostuvan polun pituutta aikatasossa. Toinen menetelmä vertaa kutakin piirreavaruudessa otettua askelta edellisiin askeliin. Kolmannessa menetelmässä määritetään dynaamisesti edellisistä piirrevektoreista konveksi kuori ja tutkitaan kuoren ulkopuolelle osuvia piirrevektoreita. Kolmas menetelmä osoittautui tutkimuksessa parhaaksi. Menetelmällä pystyttiin tunnistamaan 11 tietokannan 19:sta kohtauksista kärsineestä potilaasta. Tietokannassa on EEG-mittauksia 179 tehohoitopotilaalta. Suurin osa vääristä detektioista johtui EEG:ssä näkyvästä lihastoiminnasta, artefaktoista tai alkeellisesta tunnistuslogiikasta. Menetelmän todellista suorituskykyä on liian aikaista arvioida. Menetelmää pitää täydentää EEG-piikit sekä artefaktat luotettavasti tunnistavilla algoritmeilla.Epileptic seizures are neurological dysfunctions that are manifested in abnormal electrical activity of the brain. Behavioural correlates, such as convulsions, are sometimes associated with seizures. There are, however, seizures that do not have clear external manifestations. These non-convulsive seizures can be detected only by monitoring brain activity. Accumulating evidence suggests that non-convulsive seizures are particularly common in intensive care units (ICUs), even among patients with no prior seizures. Presence of seizures is a medical emergency that requires fast intervention. Electroencephalogram (EEG) can be used to monitor brain's electrical activity. In EEG, potential differences are measured from different sites on the subject's scalp. Long-term measurements generate a lot of data and manually reviewing all of it is an exhausting task. There is a clear need for an automatic seizure detection method. In this study, three methods are proposed for seizure detection. We compute instantaneous frequency and signal power from EEG and quantify the evolution of these features. The first method measures the length of the path that feature vectors create in the feature space. The second method compares the latest step to the average step. The last method encloses the background activity in a convex hull and classifies epochs that breach the hull. The third method was found to have the best overall performance. It can potentially detect 11 out of 19 seizure patients in the database. The database consists of recordings from 179 ICU patients. Most of the false positive detections were caused by muscle artefact, other signal artefacts, or rudimentary detection logic. The developed methods have good potential in detecting certain types of seizures. Before reporting final performance numbers, the algorithm must be comp lemented with a spike detection algorithm and a proper artefact detection algorithm