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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Fiber tractography using machine learning

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

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bioRxiv2017

Consistent local dynamics in the brain across sessions are revealed by whole brain modeling of resting state activity

Resting state fMRI has been the primary tool for studying the functional organization of the human brain. However, even at so-called \"rest\", ongoing brain activity and its underlying physiological organization is highly dynamic and yet most of the information generated so far comes from group analysis. Here we developed an imaging-based technique capable of portraying information of local dynamics at a single-subject level reliably by using a whole-brain model that estimates a local bifurcation parameter, which reflects if a brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting state sessions of one single subject and single resting state sessions from a group of 50 participants we demonstrated that individual global and local brain dynamics can be estimated consistently with respect to a reference group using only a scanning time of 15 to 20 minutes. We also showed that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger variations compared to other regions. Finally, we analyzed the variability and error of these dynamics and found high symmetry between hemispheres, which interestingly was reduced by adding more sessions. The framework presented here can be used to study functional brain dynamics on an individual level, opening new avenues for possible clinical applications.\n\nBullet pointsLocal brain dynamics are consistent across scans.\n\nFour scans of five minutes each are enough to get highly reliable and consistent results.\n\nHub areas are in a transition point between a synchronous and asynchronous regime.\n\nVariability and error of local dynamics presented high symmetry between hemispheres.

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bioRxiv2017

Multichannel Sleep Spindle Detection Using Sparse Low-Rank Optimization

BackgroundWe propose a multichannel spindle detection method that detects global and local spindle activity across all channels of scalp EEG in a single run\n\nNew MethodUsing a non-linear signal model, which assumes the multichannel EEG to be a sum of a transient component and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated multichannel oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles\n\nResults and comparison with other methodsSeveral examples are shown to illustrate the utility of the proposed method in detecting global and local spindle activity. The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4 minutes to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters\n\nConclusionsThe proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen

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bioRxiv2017

Distinct contributions by frontal and parietal cortices support working memory

Although subregions of frontal and parietal cortex both contribute and coordinate to support working memory (WM) functions, their distinct contributions remain elusive. Here, we demonstrate that perturbations to topographically organized human frontal and parietal cortex during WM maintenance cause distinct but systematic distortions in WM. The nature of these distortions supports theories positing that parietal cortex mainly codes for retrospective sensory information, while frontal cortex codes for prospective action.\n\nAcknowledgementsThe US National Institutes of Health (R01 EY016407 to C.E.C.) and the National Science Foundation (Graduate Research Fellowship Program to W.E.M.) supported the research.\n\nAuthor ContributionsW.E.M and C.E.C. designed the experiment, collected and analyzed the data, and wrote the manuscript.\n\nConflict of interestThe authors declare no competing financial interests.

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bioRxiv2017

Ultrasound elicits behavioral responses through mechanical effects on neurons and ion channels in a simple nervous system

Focused ultrasound has been shown to stimulate excitable cells, but the biophysical mechanisms behind this phenomenon remain poorly understood. To provide additional insight, we devised a behavioral-genetic assay applied to the well-characterized nervous system of C. elegans nematodes. We found that pulsed ultrasound elicits robust reversal behavior in wild-type animals in a pressure-, duration-, and pulse protocol-dependent manner. Responses were preserved in mutants unable to sense thermal fluctuations and absent in mutants lacking neurons required for mechanosensation. Additionally, we found that the worm s response to ultrasound pulses rests on the expression of MEC-4, a DEG/ENaC/ASIC ion channel required for touch sensation. Consistent with prior studies of MEC-4-dependent currents in vivo, the worms response was optimal for pulses repeated 300 to 1000 times per second. Based on these findings, we conclude that mechanical, rather than thermal stimulation accounts for behavioral responses. Further, we propose that acoustic radiation force governs the response to ultrasound in a manner that depends on the touch receptor neurons and MEC-4-dependent ion channels. Our findings illuminate a complete pathway of ultrasound action, from the forces generated by propagating ultrasound to an activation of a specific ion channel. The findings further highlight the importance of optimizing ultrasound pulsing protocols when stimulating neurons via ion channels with mechanosensitive properties.\n\nSignificance StatementHow ultrasound influences neurons and other excitable cells has remained a mystery for decades. Although it is widely understood that ultrasound can heat tissues and induce mechanical strain, whether or not neuronal activation depends on heat, mechanical force, or both physical factors is not known. We harnessed C. elegans nematodes and their extraordinary sensitivity to thermal and mechanical stimuli to address this question. Whereas thermosensory mutants respond to ultrasound similar to wild-type animals, mechanosensory mutants were insensitive to ultrasound stimulation. Additionally, stimulus parameters that accentuate mechanical effects were more effective than those producing more heat. These findings highlight a mechanical nature of the effect of ultrasound on neurons and suggest specific ways to optimize stimulation protocols in specific tissues.

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bioRxiv2017

Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

Here, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

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bioRxiv2017

Auditory prediction errors as individual biomarkers of schizophrenia

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia.\n\nEEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration-the deviant stimulus was twice the duration of the standard; 2) monaural gap-deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90{degrees} away from the standards.\n\nWe used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GPC regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006)

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bioRxiv2017

A spiral attractor network drives locomotion in Aplysia

The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysias pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit -a spiral -in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

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bioRxiv2017

NMDAR hypofunction increases top-down influence on sensory processing

Converging evidence implicates NMDAR disruption in the pathogenesis of schizophrenia, a condition in which perceptual disturbances are prominent. To explore how NMDAR hypofunction causes perceptual symptoms we investigated activity in cortical sensory circuits in awake behaving mice during pharmacologically induced NMDAR hypofunction. We observed a reduction in sensory-driven activity in V1 while input from the anterior cingulate cortex simultaneously increased, suggesting NMDAR hypofunction may lead to altered perception by modifying the balance of top-down and bottom-up processing.

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bioRxiv2017

Process reveals structure: How a network is traversed mediates expectations about its architecture

Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in how robustly knowledge of network architecture is acquired.

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bioRxiv2017

Generalized Leaky Integrate-And-Fire Models Classify Multiple Neuron Types

In the mammalian neocortex, there is a high diversity of neuronal types. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a series of generalized integrate-and-fire (GLIF) models of increasing complexity aiming to reproduce the spiking behaviors of the recorded neurons. We test the performance of these GLIF models on data from 771 neurons from 14 transgenic lines, with increasing model performance for more complex models. To answer how complex a model needs to be to reproduce the number of electrophysiological cell types, we perform unsupervised clustering on the parameters extracted from these models. The number of clusters is smaller for individual model types, but when combining all GLIF parameters 18 clusters are obtained, while 11 clusters are obtained using 16 electrophysiological features. Therefore, these low dimensional models have the capacity to characterize the diversity of cell types without the need for a priori defined features.

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bioRxiv2017

Engaging narratives evoke similar neural activity and lead to similar time perception

It is said that we lose track of time - that \"time flies\" - when we are engrossed in a story. How does engagement with the story cause this distorted perception of time, and what are its neural correlates? People commit both time and attentional resources to an engaging stimulus. For narrative videos, attentional engagement can be represented as the level of similarity between the electroencephalographic responses of different viewers. Here we show that this measure of neural engagement predicted the duration of time that viewers were willing to commit to narrative videos. Contrary to popular wisdom, engagement did not distort the average perception of time duration. Rather, more similar brain responses resulted in a more uniform perception of time across viewers. These findings suggest that by capturing the attention of an audience, narrative videos bring both neural processing and the subjective perception of time into synchrony.

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bioRxiv2017

Working memory and decision making in a fronto-parietal circuit model

Working memory (WM) and decision making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal and prefrontal cortices (PPC and PFC) at the core. However, the shared and distinct roles of these areas and the nature of their coordination in cognitive function remain poorly understood. Biophysically-based computational models of cortical circuits have provided insights into the mechanisms supporting these functions, yet they have primarily focused on the local microcircuit level, raising questions about the principles for distributed cognitive computation in multi-regional networks. To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and neuronal features of fronto-parietal circuits across multiple WM and DM paradigms. In the context of WM, both areas exhibit persistent activity, but in response to intervening distractors, PPC transiently encodes distractors, while PFC filters distractors and supports WM robustness. With regards to DM, the PPC module generates graded representations of accumulated evidence supporting target selection, while the PFC module generates more categorical responses related to action or choice. These findings suggest computational principles for distributed, hierarchical processing in cortex during cognitive function, and provide a framework for extension to multi-regional models.

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bioRxiv2017

Conserved properties of Drosophila Insomniac link sleep regulation and synaptic function

Sleep is an ancient animal behavior that is regulated similarly in species ranging from flies to mammals. Various genes that regulate sleep have been identified in invertebrates, but whether the functions of these genes are conserved in mammals remains poorly explored. Drosophila insomniac (inc) mutants exhibit severely shortened and fragmented sleep. Inc protein physically associates with the Cullin-3 (Cul3) ubiquitin ligase, and neuronal depletion of Inc or Cul3 strongly curtails sleep, suggesting that Inc is a Cul3 adaptor that directs the ubiquitination of neuronal substrates that regulate sleep. Three proteins similar to Inc exist in vertebrates--KCTD2, KCTD5, and KCTD17--but are uncharacterized within the nervous system and their functional conservation with Inc has not been addressed. Here we show that Inc and its mouse orthologs exhibit striking biochemical and functional interchangeability within Cul3 complexes. Remarkably, KCTD2 and KCTD5 restore sleep to inc mutants, indicating that they can substitute for Inc in vivo and engage its neuronal targets that impact sleep. Inc and its orthologs traffic similarly within fly and mammalian neurons and are present at synapses, suggesting that their substrates include synaptic proteins. Consistent with such a mechanism, inc mutants exhibit defects in synaptic structure and physiology, indicating that Inc is vital for both synaptic function and sleep. Our findings reveal that molecular functions of Inc are conserved through ~600 million years of evolution and support the hypothesis that Inc and its orthologs participate in an evolutionarily conserved ubiquitination pathway that links synaptic function and sleep regulation.\n\nAuthor summarySleep is ubiquitous among animals and is regulated in a similar manner across phylogeny, but whether conserved molecular mechanisms govern sleep is poorly defined. The Insomniac protein is vital for sleep in Drosophila and is a putative adaptor for the Cul3 ubiquitin ligase. We show that two mammalian orthologs of Insomniac can restore sleep to flies lacking Insomniac, indicating that the molecular functions of these proteins are conserved through evolution. Our comparative analysis reveals that Insomniac and its mammalian orthologs localize to neuronal synapses and that Insomniac impacts synaptic structure and physiology. Our findings suggest that Insomniac and its mammalian orthologs are components of an evolutionarily conserved ubiquitination pathway that links synaptic function and the regulation of sleep.

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bioRxiv2017

Cerebellar volume in autism: Meta-analysis and analysis of the ABIDE cohort

Cerebellar volume abnormalities have been often suggested as a possible endophenotype for autism spectrum disorder (ASD). We aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature, and an analysis of the Autism Brain Imaging Data Exchange (ABIDE) cohort. Our meta-analysis sought to determine a combined effect size of ASD diagnosis on different measures of the cerebellar anatomy, as well as the effect of possible factors of variability across studies. We then analysed the cerebellar volume of 328 patients and 353 controls from the ABIDE project. The meta-analysis of the literature suggested a weak but significant association between ASD diagnosis and increased cerebellar volume (p=0.049, uncorrected), but the analysis of ABIDE did not show any relationship. The studies in the literature were generally underpowered, however, the number of statistically significant findings was larger than expected. Although we could not provide a conclusive explanation for this excess of significant findings, our analyses would suggest publication bias as a possible reason. Finally, age, sex and IQ were important sources of cerebellar volume variability, however, independent of autism diagnosis.

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bioRxiv2017

Music-induced analgesia in chronic pain conditions: a systematic review and meta-analysis

Music is increasingly used as an adjuvant for chronic pain management as it is not invasive, inexpensive, and patients usually report positive experiences with it. However, little is known about its clinical efficacy in chronic pain patients. In this systematic review and meta-analysis, we investigated randomized controlled trials (RCTs) of adult patients that reported any type of music intervention for chronic pain, chosen by the researcher or patient, lasting for any duration. Searches were performed using PsycINFO, Scopus and PubMed for RTCs published until the end of May 2016. The primary outcome was reduction in self-reported pain using a standardized pain measurement instrument reported post-intervention. The secondary outcomes were: quality of life measures, depression and anxiety measures, among others. The study was pre-registered with PROSPERO (CRD42016039837) and the meta-analysis was done using RevMan. We identified 768 titles and abstracts, and we included 14 RTCs that fulfilled our criteria. The sample size of the studies varied between 25 and 200 participants. We found that music reduced chronic pain, and depression, with higher effect size on pain and depression. We also found music had a higher effect when the participant chose the music in contrast with researcher-chosen music. The sample size of RCTs was small and sometimes with different outcome measures. Our analysis suggests that music may be beneficial as an adjuvant for chronic pain patients, as it reduces self-reported pain and its common co-morbidities. Importantly, the analgesic effect of music appears higher with self-chosen over researcher-chosen music.

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bioRxiv2017

The rhythm of attentional stimulus selection during visual competition

Recent research indicates that attentional stimulus selection could in fact be a rhythmic process, operating as a sequence of successive cycles. When two items must be monitored, an intriguing corollary of this \"blinking spotlight\" notion could be that the successive cycles are directed alternately to each target; as a result, each item would effectively be selected at half the intrinsic rate of attentional selection. Here, we tested this prediction in two experiments. In an endogenous attention task, subjects covertly monitored one or two peripheral images in order to detect a brief contrast change. In the sustained occipital EEG power spectrum, selecting two vs. one item resulted in a relative increase around 4Hz and a relative decrease around 10-11Hz. In a second experiment, we tested if comparable oscillations could be observed in the stimulus-evoked EEG visual representational content. Subjects saw a first peripheral image displayed alone for 600ms, before a second one also appeared for the same duration, but at a different peripheral location. Using pattern analysis on EEG evoked-responses, we were able to create item selective classifiers that constantly indicated which stimulus was on the screen. The time-course of single-trial classifier decision values presented a relative spectral peak around 11Hz when only one object was present, and around 4-5Hz when two objects were on the screen. These results are both compatible with an attentional stimulus selection process sampling the visual field at around 10-11Hz, and resulting in a half-frequency effective sampling around 4-5Hz when there are two items to monitor.

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bioRxiv2017

Progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer’s disease

Alzheimers disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1=36, healthy control subjects, Control), G2 (N2=36, early mild cognitive impairment, EMCI), G3 (N3=36, late mild cognitive impairment, LMCI) and G4 (N4=36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I 3(Control vs EMCI), stage II (Control vs LMCI) and stage III (Control vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks,including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

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