Biology subjects

Maier, A.

Publications and source records attributed to Maier, A..

25 records · Page 2Linked to original sources

bioRxiv2021

Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

pathology↗

bioRxiv2020

Analysis and Visualization of Sleep Stages based on Deep Neural Networks

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages, would save human resources and thus would simplify clinical routines. Due to novel open-source software libraries for Machine Learning in combination with enormous progress in hardware development in recent years a paradigm shift in the field of sleep research towards automatic diagnostics could be observed. We argue that modern Machine Learning techniques are not just a tool to perform automatic sleep stage classification but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, in a way so that we can already make first assessments on sleep health in terms of sleep-apnea and consequently daytime vigilance. In the following study, we further developed our method by the innovative approach to analyze cortical activity during sleep by computing vectorial cross-correlations of different EEG channels represented by hypnodensity graphs. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.

neuroscience↗

bioRxiv2020

Analysis of ongoing neuronal activity evoked by continuous speech with computational corpus linguistics methods

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational linguistics analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.

neuroscience↗

bioRxiv2019

Stimulus-specific information flow across the canonical cortical microcircuit

The vast majority of mammalian neocortex consists of a stereotypical microcircuit, the canonical cortical microcircuit (CCM), consisting of a granular input layer, positioned between superficial and deep layers. Due to this uniform layout, neuronal activation tends to follow a similar laminar sequence, with unique information extracted at each step. For example, the primate primary visual cortex (V1) combines the two eyes signals, extracts stimulus orientation and modulates its activity depending on stimulus history. Several theories have been proposed on when and where these processes happen within the CCMs laminar activation sequence, but it has been methodologically challenging to test these hypotheses. Here, we use time-resolved multivariate pattern analysis (MVPA) to decode information regarding the eye-of-origin, stimulus orientation and stimulus repetition from simultaneously measured spiking responses across V1s laminar microcircuit. We find that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1, consistent with the notion that two eyes signals are combined within the upper layers. Conversely, orientation information was transient and equally pronounced across the microcircuit, in line with the idea that this information is relayed to other areas for further processing. Moreover, when stimuli were repeated, information regarding orientation was enhanced at the expense of eye-of origin information, suggesting that V1 modulates information flow to optimize specific stimulus dimensions. Taken together, these findings provide empirical evidence that adjudicates between long-standing hypotheses and reveals how information transfer within the CCM supports unique cortical functions.\n\nSignificance StatementDespite the brains daunting complexity, there are common organizing principles across brain areas. For example, neocortical activation follows a stereotypical pattern that spreads from input layers towards layers above and below. While this activation pattern is well known, it has been challenging to ascertain how unique types of information are extracted within this common sequence in different brain areas. Here we use machine learning to track the flow of stimulus-specific information across the layers of visual cortex. We found that information regarding several separate stimulus dimensions was routed uniquely within the common activation sequence in a manner that confirmed prior model predictions. This finding demonstrates how differences in information flow within the stereotypical neocortical activation sequence shape area-specific functions.

neuroscience↗

bioRxiv2018

Binocular modulation of monocular V1 neurons

In humans and other primates, sensory signals from each eye remain separated until they arrive in the primary visual cortex (V1), but their exact meeting point is unknown. In V1, some neurons are activated by stimulation of only one eye (monocular neurons) while most neurons are driven by stimulation of either eye (binocular neurons). Monocular neurons are most prevalent in the main input layers of V1 while binocular neurons dominate the layers above and below. This observation has given rise to the idea that the two eyes signals remain separate until they converge outside V1s input layers. Here, we show that despite responding to only one eye, monocular neurons in all layers, including the input layers, of V1 discriminate between stimulation of their driving eye alone and stimulation of both eyes. This finding suggests that binocular signals occur at an earlier processing stage than previously appreciated as even so-called monocular neurons across all V1 layers encode what is shown to both eyes.

neuroscience↗

bioRxiv2018

Adaptation of Intracortical Signaling Concurs with Enhanced Encoding Efficiency

Stimulus repetitions improve performance despite decreased brain responses, suggesting that the brain is more efficient when processing familiar stimuli. Previous work demonstrated that stimulus repetition enhances encoding efficiency in primary visual cortex (V1) by increasing synchrony and sharpening the orientation tuning of neurons. Here we show that these adaptive changes are supported by an altered flow of sensory activation across the V1 laminar microcircuit. Using a repeating stimulus sequence, we recorded laminar responses in V1 of two fixating monkeys. We found repetition-related response reductions that were most pronounced outside V1 layers that receive the main retinogeniculate input. This repetition-induced suppression was robust to alternating stimuli between the eyes, in line with the notion that repetition suppression is predominantly of cortical origin. Congruent with earlier reports, we found that V1 adaptation to repeating stimuli is accompanied by sharpened neural tuning as well as increased neural synchrony. Current source density (CSD) analysis, which provides an estimate of net synaptic activation, revealed that the responses to repeated stimuli were most profoundly affected within layers that harbor the bulk of cortico-cortical connections. Together, these results suggest that stimulus repetition induces an altered state of intracortical processing resulting in enhanced encoding efficiency of sensory stimuli.

neuroscience↗

bioRxiv2018

Binocular Integration Manifests as a Transient Spiking Increase Followed by Selective Suppression in Primary Visual Cortex

Research throughout the past decades revealed that neurons in primate primary visual cortex (V1) rapidly integrate the two eyes separate signals into a combined binocular response. The exact mechanisms giving underlying this binocular integration remain elusive. One open question is whether binocular integration occurs at a single stage of sensory processing or in a sequence of computational steps. To address this question, we examined the temporal dynamics of binocular integration across V1s laminar microcircuit of awake behaving monkeys. We find that V1 processes binocular stimuli in a dynamic sequence that comprises at least two distinct phases: A transient phase, lasting 50-150ms from stimulus onset, in which neuronal population responses are significantly enhanced for binocular stimulation compared to monocular stimulation, followed by a sustained phase characterized by widespread suppression in which feature-specific computations emerge. In the sustained phase, incongruent binocular stimulation resulted in response reduction relative to monocular stimulation across the V1 population. By contrast, sustained responses for binocular congruent stimulation were either reduced or enhanced relative to monocular responses depending on the neurons selectivity for one or both eyes (i.e., ocularity). These results suggest that binocular integration in V1 occurs in at least two sequential steps, with an initial additive combination of the two eyes signals followed by the establishment of interocular concordance and discordance.\n\nSignificance StatementOur two eyes provide two separate streams of visual information that are merged in the primary visual cortex (V1). Previous work showed that stimulating both eyes rather than one eye may either increase or decrease activity in V1, depending on the nature of the stimuli. Here we show that V1 binocular responses change over time, with an early phase of general excitation and followed by stimulus-dependent response suppression. These results provide important new insights into the neural machinery that supports the combination of the two eyes perspectives into a single coherent view.

neuroscience↗