Trauma-Associated Tinnitus: Audiological, Demographic and Clinical Characteristics

Background: Tinnitus can result from different etiologies. Frequently, patients report the development of tinnitus after traumatic injuries. However, to which extent this specific etiologic factor plays a role for the phenomenology of tinnitus is still incompletely understood. Additionally, it remains a matter of debate whether the etiology of tinnitus constitutes a relevant criterion for defining tinnitus subtypes. Objective: By investigating a worldwide sample of tinnitus patients derived from the Tinnitus Research Initiative (TRI) Database, we aimed to identify differences in demographic, clinical and audiological characteristics between tinnitus patients with and without preceding trauma. Materials: A total of 1,604 patients were investigated. Assessment included demographic data, tinnitus related clinical data, audiological data, the Tinnitus Handicap Inventory, the Tinnitus Questionnaire, the Beck Depression Inventory, various numeric tinnitus rating scales, and the World Health Organisation Quality of Life Scale (WHOQoL). Results: Our data clearly indicate differences between tinnitus patients with and without trauma at tinnitus onset. Patients suffering from trauma-associated tinnitus suffer from a higher mental burden than tinnitus patients presenting with phantom perceptions based on other or unknown etiologic factors. This is especially the case for patients with whiplash and head trauma. Patients with posttraumatic noise-related tinnitus experience more frequently hyperacousis, were younger, had longer tinnitus duration, and were more frequently of male gender. Conclusions: Trauma before tinnitus onset seems to represent a relevant criterion for subtypization of tinnitus. Patients with posttraumatic tinnitus may require specific diagnostic and therapeutic management. A more systematic and - at best - standardized assessment for hearing related sequelae of trauma is needed for a better understanding of the underlying pathophysiology and for developing more tailored treatment approaches as well.Fil: Kreuzer, Peter M.. Universitat Regensburg; AlemaniaFil: Landgrebe, Michael. Universitat Regensburg; AlemaniaFil: Schecklmann, Martin. Universitat Regensburg; AlemaniaFil: Staudinger, Susanne. Universitat Regensburg; AlemaniaFil: Langguth, Berthold. Universitat Regensburg; AlemaniaFil: Vielsmeier, Veronika. The TRI Database Study Group; AlemaniaFil: Kleinjung, Tobias. The TRI Database Study Group; AlemaniaFil: Lehner, Astrid. The TRI Database Study Group; AlemaniaFil: Poeppl, Timm B.. The TRI Database Study Group; AlemaniaFil: Figueiredo, Ricardo. The TRI Database Study Group; AlemaniaFil: Azevedo, Andréia. The TRI Database Study Group; AlemaniaFil: Binetti, Ana Carolina. The TRI Database Study Group; AlemaniaFil: Elgoyhen, Ana Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina. The TRI Database Study Group; AlemaniaFil: Rates, Marcelo. The TRI Database Study Group; AlemaniaFil: Coelho, Claudia. The TRI Database Study Group; AlemaniaFil: Vanneste, Sven. The TRI Database Study Group; AlemaniaFil: de Ridder, Dirk. The TRI Database Study Group; AlemaniaFil: van de Heyning, Paul. The TRI Database Study Group; AlemaniaFil: Zeman, Florian. The TRI Database Study Group; AlemaniaFil: Mohr, Markus. The TRI Database Study Group; AlemaniaFil: Koller, Michael. The TRI Database Study Group; Alemani

Computational modelling of visual illusions

Thesis by publication.Includes bibliographical references.1. Introduction -- 2. Study 1 -- 3. Study 2 -- 4. Study 3 -- 5. Discussion & conclusion.Illusions reveal some of the sophisticated, underlying neural mechanisms that often remain hidden in our day-to-day visual experience. Illusions have traditionally been studied using psychological methods, which quantify overall, system-level effects observable at the highest layer of the visual hierarchy. This thesis applies the relatively new technique of computational modelling to the study of visual illusions, to quantify bias and uncertainty within various levels of our visual system. The method adopted in this thesis merges statistical inferences, obtained from exposure to image subsets, with filtering operations that mimic visual neural processing from layer to layer. Previous computational models of visual illusions have considered these in isolated arrangement. This dissertation highlights the benefits of combinatorial modelling, which includes separating out the contribution of neural operations from potential statistical influences.The first study in this dissertation investigates a well-known line-length illusion in a benchmark model of the visual ventral stream, demonstrating that a model imitating the structure and function of our cortical visual system is susceptible to illusions. In the second study, we further scrutinise this line-length illusion inside each layer of the benchmark model, observing magnitudes of uncertainty and bias that propagate through each level. In the third and final study, we introduce a new model based on exponential filters inspired by contrast statistics of natural images. We apply a suite of lightness illusions to this new model and demonstrate that low-level kernel operations can account for a large set of these illusions. In summary, this thesis shows that combining filtering functions with natural image statistics not only allows for illusory bias and uncertainty to be limited in artificial neural network models. but it also provides further evidence for and against some proposed theories of visual illusions.Mode of access: World wide web1 online resource (209 pages) illustrations (some colour

Predicting Cluster Formation in Decentralized Sensor Grids

Abstract. This paper investigates cluster formation in decentralized sensor grids and focusses on predicting when the cluster formation converges to a stable configuration. The traffic volume of inter-agent communications is used, as the underlying time series, to construct a predictor of the convergence time. The predictor is based on the assumption that decentralized cluster formation creates multiagent chaotic dynamics in the communication space, and estimates irregularity of the communication-volume time series during an initial transient interval. The new predictor, based on the auto-correlation function, is contrasted with the predictor based on the correlation entropy (generalized entropy rate). In terms of predictive power, the auto-correlation function is observed to outperform and be less sensitive to noise in the communication space than the correlation entropy. In addition, the preference of the auto-correlation function over the correlation entropy is found to depend on the synchronous message monitoring method.

Reproducing the Muller-Lyer illusion in a computational feed-forward object recognition model

The Muller-Lyer illusion is where the perceived length of lines is altered when the shafts are terminated by various fins. Lines appear shorter in a 'wings-in' configuration versus longer in a 'wings-out' configuration. Explanations for this effect range from low level signal processing to the misapplication of rules from higher cognitive areas. Computer models that mimic visual processing allow for some of these proposed contributing factors to be tested in isolation. The HMAX model is a current state-of-the-art object recognition model that is also biologically plausible [Mutch and Lowe, 2008 International Journal of Computer Vision 80(1) 45-57]. We trained this model to perform a dual categorisation task based on relative line lengths within an image. We then measured the accuracy of the system in categorising control images versus illusory images. Our results indicate this feed-forward model replicated an overall illusory effect.1 page(s

An Exponential filter model predicts lightness illusions

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.15 page(s

Complex cells decrease errors for the Müller-Lyer illusion in a model of the visual ventral stream

To improve robustness in object recognition, many artificial visual systems imitate the way in which the human visual cortex encodes object information as a hierarchical set of features. These systems are usually evaluated in terms of their ability to accurately categorize well-defined, unambiguous objects and scenes. In the real world, however, not all objects and scenes are presented clearly, with well-defined labels and interpretations. Visual illusions demonstrate a disparity between perception and objective reality, allowing psychophysicists to methodically manipulate stimuli and study our interpretation of the environment. One prominent effect, the Muller-Lyer illusion, is demonstrated when the perceived length of a line is contracted (or expanded) by the addition of arrowheads (or arrow-tails) to its ends. HMAX, a benchmark object recognition system, consistently produces a bias when classifying Muller-Lyer images. HMAX is a hierarchical, artificial neural network that imitates the "simple” and "complex” cell layers found in the visual ventral stream. In this study, we perform two experiments to explore the Muller-Lyer illusion in HMAX, asking: (1) How do simple vs. complex cell operations within HMAX affect illusory bias and precision? (2) How does varying the position of the figures in the input image affect classification using HMAX? In our first experiment, we assessed classification after traversing each layer of HMAX and found that in general, kernel operations performed by simple cells increase bias and uncertainty while max-pooling operations executed by complex cells decrease bias and uncertainty. In our second experiment, we increased variation in the positions of figures in the input images that reduced bias and uncertainty in HMAX. Our findings suggest that the Muller-Lyer illusion is exacerbated by the vulnerability of simple cell operations to positional fluctuations, but ameliorated by the robustness of complex cell responses to such variance

The Müller-Lyer Illusion in a computational model of biological object recognition.

Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI, such as misapplied size constancy scaling, the statistics of image-source relationships and the filtering properties of signal processing in primary visual areas. Artificial models of the ventral visual processing stream allow us to isolate factors hypothesised to cause the illusion and test how these affect classification performance. We trained a feed-forward feature hierarchical model, HMAX, to perform a dual category line length judgment task (short versus long) with over 90% accuracy. We then tested the system in its ability to judge relative line lengths for images in a control set versus images that induce the MLI in humans. Results from the computational model show an overall illusory effect similar to that experienced by human subjects. No natural images were used for training, implying that misapplied size constancy and image-source statistics are not necessary factors for generating the illusion. A post-hoc analysis of response weights within a representative trained network ruled out the possibility that the illusion is caused by a reliance on information at low spatial frequencies. Our results suggest that the MLI can be produced using only feed-forward, neurophysiological connections

Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex

Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with neural representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.status: publishe

HMAX Model architecture.

<p>Information flows unidirectionally through the hierarchical layers. Input to the system is a 256×256 greyscale image and the output is a classification of the image as LONG or SHORT. The input image is first transformed onto multiple scales via the Image Layer. The following four layers alternate in their functionality, dedicated to template matching (S layers) or feature pooling (C layers). The final SVM layer performs binary classification.</p

Experiment III: Illusion Strength Affected by Angle.

<p>Results here are plotted as psychometric curves with values on the left representing the SHORT condition, and values on the right representing the LONG condition. The control condition with all angles collapsed shows no bias. For illusory lines with 40 degree fins we see a PSE of approximately 12 pixels. Illusory lines with 20 degree fins show a larger PSE, congruent with human data. Illusory lines with 60 degree fins no longer demonstrate an illusory effect, indicated by intersection of the curve through 50% when the line length difference is zero.</p