scholarly journals Discovery of a new song mode in Drosophila reveals hidden structure in the sensory and neural drivers of behavior

2017 ◽  
Author(s):  
Jan Clemens ◽  
Philip Coen ◽  
Frederic A. Roemschied ◽  
Talmo Pereira ◽  
David Mazumder ◽  
...  
Keyword(s):  
Sensory Feedback ◽  
Brain Activity ◽  
Unsupervised Classification ◽  
Time Varying ◽  
Classification Methods ◽  
Wing Vibration ◽  
Genetic Mechanisms ◽  
Song Production ◽  
Sensory Neural ◽  
Female Responses

SummaryDeciphering how brains generate behavior depends critically on an accurate description of behavior. If distinct behaviors are lumped together, separate modes of brain activity can be wrongly attributed to the same behavior. Alternatively, if a single behavior is split into two, the same neural activity can appear to produce different behaviors [1]. Here, we address this issue in the context of acoustic communication in Drosophila. During courtship, males utilize wing vibration to generate time-varying songs, and females evaluate songs to inform mating decisions [2-4]. Drosophila melanogaster song was thought for 50 years to consist of only two modes, sine and pulse, but using new unsupervised classification methods on large datasets of song recordings, we now establish the existence of at least three song modes: two distinct, evolutionary conserved pulse types, along with a single sine mode. We show how this seemingly subtle distinction profoundly affects our interpretation of the mechanisms underlying song production, perception and evolution. Specifically, we show that sensory feedback from the female influences the probability of producing each song mode and that male song mode choice affects female responses and contributes to modulating his song amplitude with distance [5]. At the neural level, we demonstrate how the activity of three separate neuron types within the fly’s song pathway differentially affect the probability of producing each song mode. Our results highlight the importance of carefully segmenting behavior to accurately map the underlying sensory, neural, and genetic mechanisms.

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

2020 ◽  
Author(s):  
Alba Xifra-Porxas ◽  
Michalis Kassinopoulos ◽  
Georgios D. Mitsis
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Large Data ◽  
Head Motion ◽  
Time Varying ◽  
Resting State Networks ◽  
Human Connectome Project ◽  
Noninvasive Biomarker ◽  
Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

scholarly journals Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

2020 ◽  
Vol 12 (3) ◽  
pp. 759
Author(s):  
Jūratė Sužiedelytė Visockienė ◽  
Eglė Tumelienė ◽  
Vida Maliene
Keyword(s):  
Remote Sensing ◽  
Invasive Plant ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Economic Losses ◽  
Classification Methods ◽  
Earth Remote Sensing ◽  
Heracleum Sosnowskyi

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

scholarly journals Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1190 ◽  
Author(s):  
Denise Dettmering ◽  
Alan Wynne ◽  
Felix Müller ◽  
Marcello Passaro ◽  
Florian Seitz
Keyword(s):  
Unsupervised Classification ◽  
Detection Algorithm ◽  
Classification Method ◽  
Correct Identification ◽  
Polar Regions ◽  
Classification Methods ◽  
Lead Detection ◽  
Radar Echoes ◽  
Polar Oceans ◽  
Sar Data

In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.

scholarly journals Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging

2021 ◽  
Vol 18 (181) ◽  
pp. 20210523
Author(s):  
Nathaniel J. Linden ◽  
Dennis R. Tabuena ◽  
Nicholas A. Steinmetz ◽  
William J. Moody ◽  
Steven L. Brunton ◽  
...  
Keyword(s):  
Coherent Structures ◽  
Calcium Imaging ◽  
Large Scale ◽  
Vector Fields ◽  
Brain Activity ◽  
Time Varying ◽  
Finite Time Lyapunov Exponent ◽  
Synthetic Datasets ◽  
The Brain ◽  
Analyse Time

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits . These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.

scholarly journals A need for time-varying models to suppress artefacts of tACS in the M/EEG

2021 ◽  
Author(s):  
Nicholas S Bland
Keyword(s):  
False Positive ◽  
Brain Activity ◽  
Neural Oscillations ◽  
Time Varying ◽  
Positive Evidence ◽  
Specific Manner ◽  
Transcranial Alternating Current Stimulation ◽  
Time Invariance ◽  
Eeg Recordings ◽  
Time Varying Models

Rhythmic modulation of brain activity by transcranial alternating current stimulation (tACS) can entrain neural oscillations in a frequency- and phase-specific manner. However, large stimulation artefacts contaminate concurrent 'online' neuroimaging measures, including magneto- and electro-encephalography (M/EEG) — restricting most analyses to periods free from stimulation ('offline' aftereffects). While many published methods exist for removing artefacts of tACS from M/EEG recordings, they universally assume linear artefacts: either time-invariance (i.e., an artefact is a scaled version of itself from cycle to cycle) or sensor-invariance (i.e., artefacts are scaled versions of one another from sensor to sensor). However, heartbeat and respiration both nonlinearly modulate the amplitude and phase of these artefacts, predominantly via changes in scalp impedance. The spectral symmetry this introduces to the M/EEG spectra may lead to false-positive evidence for entrainment around the frequency of tACS, if not adequately suppressed. Good electrophysiological evidence for entrainment therefore requires that tACS artefacts are fully accounted for before comparing online spectra to a control (e.g., as might be observed during sham stimulation). Here I outline an approach to linearly solve templates for tACS artefacts, and demonstrate how event-locked perturbations to amplitude and phase can be introduced from simultaneous recordings of heartbeat and respiration — effectively forming time-varying models of tACS artefacts. These models are constructed for individual sensors, and can therefore be used in contexts with few EEG sensors and with no assumption of artefact collinearity. I also discuss the feasibility of this approach in the absence of simultaneous recordings of heartbeat and respiration traces.

scholarly journals A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ali Yener Mutlu ◽  
Edward Bernat ◽  
Selin Aviyente
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Dynamic Network ◽  
Principal Component ◽  
Brain Network ◽  
Event Related Potential ◽  
Time Varying ◽  
Error Related Negativity ◽  
Connectivity Patterns

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

Classification methods for functional data

2018 ◽  
Author(s):  
Amparo Baillo ◽  
Antonio Cuevas ◽  
Ricardo Fraiman
Keyword(s):  
Functional Data ◽  
Supervised Classification ◽  
Nearest Neighbor ◽  
Real Data ◽  
Reproducing Kernels ◽  
Unsupervised Classification ◽  
Classification Problem ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Empirical Risk

This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.

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