Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

Invariance between subjects of brain wave representations of language

In three experiments, electric brain waves of 19 subjects were recorded under several different experimental conditions for two purposes. One was to test how well we could recognize which sentence, from a set of 24 or 48 sentences, was being processed in the cortex. The other was to study the invariance of brain waves between subjects. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. In all three experiments, averaging over subjects improved the recognition rates. The most significant finding was the following. When brain waves were averaged separately for two nonoverlapping groups of subjects, one for prototypes and the other for test samples, we were able to recognize correctly 90% of the brain waves generated by 48 different sentences about European geography.

Imaging of H217O distribution in the brain of a live rat by using proton-detected 17O MRI

Imaging of H217O has a number of important applications. Mapping the distribution of H217O produced by oxidative metabolism of 17O-enriched oxygen gas may lead to a new method of metabolic functional imaging; regional cerebral blood flow also can be measured by measuring the H217O distribution after the injection of 17O-enriched physiological saline solution. Previous studies have proposed a method for indirect detection of 17O. The method is based on the shortening of the proton T2 in H217O solutions, caused by the residual 17O-1H scalar coupling and transferred to the bulk water via fast chemical exchange. It has been shown that the proton T2 of H217O solutions can be restored to that of H216O by irradiating the resonance frequency of the 17O nucleus. The indirect 17O image thus is obtained by taking the difference between two T2-weighted spin-echo images: one acquired after irradiation of the 17O resonance and one acquired without irradiation. It also has been established that, at relatively low concentrations of H217O, the indirect method yields an image that quantitatively reflects the H217O distribution in the sample. The method is referred to as PRIMO (proton imaging of oxygen). In this work, we show in vivo proton images of the H217O distribution in a rat brain after an i.v. injection of H217O-enriched physiological saline solution. Implementing the indirect detection method in an echo-planar imaging sequence enabled obtaining H217O images with good spatial and temporal resolution of few seconds.

Invariance of brain-wave representations of simple visual images and their names

In two experiments, electric brain waves of 14 subjects were recorded under several different conditions to study the invariance of brain-wave representations of simple patches of colors and simple visual shapes and their names, the words blue, circle, etc. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. The most significant results were these. By averaging over different subjects, as well as trials, we created prototypes from brain waves evoked by simple visual images and test samples from brain waves evoked by auditory or visual words naming the visual images. We correctly recognized from 60% to 75% of the test-sample brain waves. The general conclusion is that simple shapes such as circles and single-color displays generate brain waves surprisingly similar to those generated by their verbal names. These results, taken together with extensive psychological studies of auditory and visual memory, strongly support the solution proposed for visual shapes, by Bishop Berkeley and David Hume in the 18th century, to the long-standing problem of how the mind represents simple abstract ideas.

When the brain changes its mind: Interocular grouping during binocular rivalry

The prevalent view of binocular rivalry holds that it is a competition between the two eyes mediated by reciprocal inhibition among monocular neurons. This view is largely due to the nature of conventional rivalry-inducing stimuli, which are pairs of dissimilar images with coherent patterns within each eye’s image. Is it the eye of origin or the coherency of patterns that determines perceptual alternations between coherent percepts in binocular rivalry? We break the coherency of conventional stimuli and replace them by complementary patchworks of intermingled rivalrous images. Can the brain unscramble the pieces of the patchwork arriving from different eyes to obtain coherent percepts? We find that pattern coherency in itself can drive perceptual alternations, and the patchworks are reassembled into coherent forms by most observers. This result is in agreement with recent neurophysiological and psychophysical evidence demonstrating that there is more to binocular rivalry than mere eye competition.

General and specific brain regions involved in encoding and retrieval of events: what, where, and when.

Remembering an event involves not only what happened, but also where and when it occurred. We measured regional cerebral blood flow by positron emission tomography during initial encoding and subsequent retrieval of item, location, and time information. Multivariate image analysis showed that left frontal brain regions were always activated during encoding, and right superior frontal regions were always activated at retrieval. Pairwise image subtraction analyses revealed information-specific activations at (i) encoding, item information in left hippocampal, location information in right parietal, and time information in left fusiform regions; and (ii) retrieval, item in right inferior frontal and temporal, location in left frontal, and time in anterior cingulate cortices. These results point to the existence of general encoding and retrieval networks of episodic memory whose operations are augmented by unique brain areas recruited for processing specific aspects of remembered events.

Information processing in the human brain: magnetoencephalographic approach.

Rapid progress in effective methods to image brain functions has revolutionized neuroscience. It is now possible to study noninvasively in humans neural processes that were previously only accessible in experimental animals and in brain-injured patients. In this endeavor, positron emission tomography has been the leader, but the superconducting quantum interference device-based magnetoencephalography (MEG) is gaining a firm role, too. With the advent of instruments covering the whole scalp, MEG, typically with 5-mm spatial and 1-ms temporal resolution, allows neuroscientists to track cortical functions accurately in time and space. We present five representative examples of recent MEG studies in our laboratory that demonstrate the usefulness of whole-head magnetoencephalography in investigations of spatiotemporal dynamics of cortical signal processing.

Magnetic resonance imaging (MRI) detection of the murine brain response to light: temporal differentiation and negative functional MRI changes.

Using a 9.4 T MRI instrument, we have obtained images of the mouse brain response to photic stimulation during a period between deep anesthesia and the early stages of arousal. The large image enhancements we observe (often >30%) are consistent with literature results extrapolated to 9.4 T. However, there are also two unusual aspects to our findings. (i) The visual area of the brain responds only to changes in stimulus intensity, suggesting that we directly detect operations of the M visual system pathway. Such a channel has been observed in mice by invasive electrophysiology, and described in detail for primates. (ii) Along with the typical positive response in the area of the occipital portion of the brain containing the visual cortex, another area displays decreased signal intensity upon stimulation.

Activation of single whisker barrel in rat brain localized by functional magnetic resonance imaging.

The previously established cortical representation of rat whiskers in layer IV of the cortex contains distinct cylindrical columns of cellular aggregates, which are termed barrels and correlate in a one-to-one relation to whiskers on the contralateral rat face. In the present study, functional magnetic resonance imaging (fMRI) of the rat brain was used to map whisker barrel activation during mechanical up-down movement (+/- 2.5 mm amplitude at 8 Hz) of single/multiple whisker(s). Multislice gradient echo fMRI experiments were performed at 7 T with in-plane image resolution of 220 x 220 microns, slice thickness of 1 mm, and echo time of 16 ms. Highly significant (P < 0.001) and localized contralateral regions of activation were observed upon stimulation of single/multiple whisker(s). In all experiments (n = 10), the locations of activation relative to bregma and midline were highly correlated with the neuroanatomical position of the corresponding whisker barrels, and the results were reproducible intra- and interanimal. Our results indicate that fMRI based on blood oxygenation level-dependent image contrast has the sensitivity to depict activation of a single whisker barrel in the rat brain. This noninvasive technique will supplement existing methods in the study of rat barrel cortex and should be particularly useful for the long-term investigations of central nervous system in the same animal.

Identification of human brain regions underlying responses to resistive inspiratory loading with functional magnetic resonance imaging.

Compensatory ventilatory responses to increased inspiratory loading are essential for adequate breathing regulation in a number of pulmonary diseases; however, the human brain sites mediating such responses are unknown. Midsagittal and axial images were acquired in 11 healthy volunteers during unloaded and loaded (30 cmH2O; 1 cmH2O = 98 Pa) inspiratory breathing, by using functional magnetic resonance imaging (fMRI) strategies (1.5-tesla MR; repetition time, 72 msec; echo time, 45 msec; flip angle, 30 degrees; field of view, 26 cm; slice thickness, 5 mm; number of excitations, 1; matrix, 128 x 256). Digital image subtractions and region of interest analyses revealed significantly increased fMRI signal intensity in discrete areas of the ventral and dorsal pons, interpeduncular nucleus, basal forebrain, putamen, and cerebellar regions. Upon load withdrawal, certain regions displayed a rapid fMRI signal off-transient, while in others, a slower fMRI signal decay emerged. Sustained loading elicited slow decreases in fMRI signal across activated regions, while second application of an identical load resulted in smaller signal increases compared to initial signal responses (P < 0.001). A moderate inspiratory load is associated with consistent regional activation of discrete brain locations; certain of these regions have been implicated in mediation of loaded breathing in animal models. We speculate that temporal changes in fMRI signal may indicate respiratory after-discharge and/or habituation phenomena.

Classifying image analysis techniques from their output

In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Assessment of fight outcome is needed to activate socially driven transcriptional changes in the zebrafish brain

Group living animals must be able to express different behavior profiles depending on their social status. Therefore, the same genotype may translate into different behavioral phenotypes through socially driven differential gene expression. However, how social information is translated into a neurogenomic response and what are the specific cues in a social interaction that signal a change in social status are questions that have remained unanswered. Here, we show for the first time, to our knowledge, that the switch between status-specific neurogenomic states relies on the assessment of fight outcome rather than just on self- or opponent-only assessment of fighting ability. For this purpose, we manipulated the perception of fight outcome in male zebrafish and measured its impact on the brain transcriptome using a zebrafish whole genome gene chip. Males fought either a real opponent, and a winner and a loser were identified, or their own image on a mirror, in which case, despite expressing aggressive behavior, males did not experience either a victory or a defeat. Massive changes in the brain transcriptome were observed in real opponent fighters, with losers displaying both a higher number of differentially expressed genes and of coexpressed gene modules than winners. In contrast, mirror fighters expressed a neurogenomic state similar to that of noninteracting fish. The genes that responded to fight outcome included immediate early genes and genes involved in neuroplasticity and epigenetic modifications. These results indicate that, even in cognitively simple organisms such as zebrafish, neurogenomic responses underlying changes in social status rely on mutual assessment of fighting ability.