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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

The neural basis of precise visual short-term memory for complex recognisable objects

Recent evidence suggests that visual short-term memory (VSTM) capacity estimated using simple objects, such as colours and oriented bars, may not generalise well to more naturalistic stimuli. More visual detail can be stored in VSTM when complex, recognisable objects are maintained compared to simple objects. It is not yet known if it is recognisability that enhances memory precision, nor whether maintenance of recognisable objects is achieved with the same network of brain regions supporting maintenance of simple objects.\n\nWe used a novel stimulus generation method to parametrically warp photographic images along a continuum, allowing separate estimation of the precision of memory representations and the number of items retained. The stimulus generation method was also designed to create unrecognisable, though perceptually matched, stimuli, to investigate the impact of recognisability on VSTM. We adapted the widely-used change detection and continuous report paradigms for use with complex, photographic images.\n\nAcross three functional magnetic resonance imaging (fMRI) experiments, we demonstrated greater precision for recognisable objects in VSTM compared to unrecognisable objects. This clear behavioural advantage was not the result of recruitment of additional brain regions, or of stronger mean activity within the core network. Representational similarity analysis revealed greater variability across item repetitions in the representations of recognisable, compared to unrecognisable complex objects. We therefore propose that a richer range of neural representations support VSTM for complex recognisable objects.

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bioRxiv2016

Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

Resting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).

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bioRxiv2016

The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition

To characterize the left-ventral occipito-temporal cortex (lvOT) role during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filter out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging. Empirically, using functional magnetic resonance imaging, we demonstrate that quantitative LCM simulations predict lvOT activation across three studies better than alternative models. Besides, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT. In contrast, a dichotomous word/non-word contrast, which is assumed as the LCMs output, could be localized to upstream frontal brain regions. Finally, we found that training lexical categorization results in more efficient reading. Thus, we propose a ventral-visual-stream processing framework for reading involving word-likeness extraction followed by lexical categorization, before meaning extraction.

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bioRxiv2016

Transcriptomics reveals patterns of sexually dimorphic gene expression in an avian hypothalamic-pituitary-gonadal (HPG) axis

The hypothalamic-pituitary-gonadal (HPG) axis is a key biological system required for reproduction and associated sexual behaviors to occur. Each of these tissues, the hypothalamus in the brain, the pituitary gland, and the gonads (testes and ovaries), has specialized and sometimes sex-specific functions that promote, amongst other things, reproductive processes. To fully characterize the transcript community of each aspect of the HPG axis, as well as to uncover potentially important sex-specific differences, we characterized patterns of gene expression in sexually mature male and female rock dove (Columba livia) hypothalamus, pituitary, and gonads. We describe patterns of gene expression amongst tissues as well as patterns of sex-biased gene expression which may underlie some of the fundamental differences between male and female reproductive behavior. We report greater sex-biased differential expression in the pituitary (233 genes out of 15,102 genes) as compared to the hypothalamus (1 gene out of 15,102 genes), with multiple genes overexpressed in the male pituitary gland being related to locomotion, and multiple genes over-expressed in the female pituitary gland being related to reproduction, growth and development. These genes may be associated with facilitating the different roles the HPG system plays in sex-specific reproductive behavior, including life-history strategies characterized by short-term payoffs in males (i.e. locomotion) and longer-term payoffs in females (i.e. development and reproduction). In addition, we report novel patterns of sex-biased expression in genes involved in reproduction-associated processes. Gonadotropin-releasing hormone, Progesterone, and Androgen receptor were upregulated in the female pituitary as compared to the male pituitary. We also found greater expression of Prolactin in the female pituitary as compared to the male pituitary, but it was more highly expressed in the male hypothalamus as compared to the female hypothalamus. The Prolactin receptor was also more highly expressed in the male hypothalamus and pituitary gland as compared to corresponding female tissues. Finally, we discovered greater expression of Arginine Vasopressin Receptor 1A in the male pituitary as compared to the female pituitary. Using a more global analysis, we discovered other interesting sex-biased patterns in genes not originally targeted for investigation. These genes include like Betacellulin (BTC), which was upregulated in the female pituitary and compared to males, and Ecto-NOX Disulfide-Thiol Exchanger 1 (ENOX1), which was upregulated in the male pituitary as compared to females. These genes may play important, though currently unknown, roles in reproductive physiology and behavior. In conclusion, we reveal patterns of tissue specific and sexually dimorphic gene expression in the HPG axis, highlighting the need for sex parity in transcriptomic studies and providing new lines of investigation of the mechanisms of reproductive function.

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bioRxiv2016

Feature-coding transitions to conjunction-coding with progression through human visual cortex

Identifying an object and distinguishing it from similar items depends upon the ability to perceive its component parts as conjoined into a cohesive whole, but the brain mechanisms underlying this ability remain elusive. The ventral visual processing pathway in primates is organized hierarchically: Neuronal responses in its early stages are sensitive to the manipulation of simple visual features whereas neuronal responses in subsequent stages are tuned to increasingly complex stimulus attributes. It is widely assumed that feature-coding dominates in early visual cortex whereas later visual regions employ conjunction-coding in which object representations are different from the sum of their simple-feature parts. However, no study has demonstrated that putative object-level codes in higher visual cortex cannot be accounted for by feature-coding and that putative feature-codes in regions prior to ventral temporal cortex are not equally well characterized as object-level codes. Thus the existence of a transition from feature- to conjunction-coding in visual cortex remains unconfirmed, and, if a transition does occur, its location remains unknown. By employing multivariate analysis of human functional imaging data, we measure both feature-coding and conjunction-coding directly, using the same set of visual stimuli, and pit them against each other to reveal the relative dominance of one versus the other throughout cortex. We provide the first demonstration of a transition from feature-coding in early visual cortex to conjunction-coding in both inferior temporal and posterior parietal cortices. This novel method enables the use of experimentally controlled stimulus features to investigate population-level feature- and conjunction-codes throughout human cortex.

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bioRxiv2016

Predicting brain-age from multimodal imaging data captures cognitive impairment

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brainimaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.\n\nHighlightsO_LIBrain-based age prediction is improved with multimodal neuroimaging data.\nC_LIO_LIParticipants with cognitive impairment show increased brain aging.\nC_LIO_LIAge prediction models are robust to motion and generalize to independent datasets from other sites.\nC_LI

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bioRxiv2016

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal populations; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations in brain dynamics (also called functional connectivity), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics at each brain region by computing 6 930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag{tau} = 34s (partial Spearman correlation{rho} = 0.58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz:{rho} = -0.43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

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bioRxiv2016

Bridging multiple scales in the human brain using computational modelling

Brain dynamics span multiple spatial and temporal scales, from fast spiking neurons to slow fluctuations over distributed areas. No single experimental method links data across scales. Here, we bridge this gap using The Virtual Brain connectome-based modelling platform to integrate multimodal data with biophysical models and support neurophysiological inference. Simulated cell populations were linked with subject-specific white-matter connectivity estimates and driven by electroencephalography-derived electric source activity. The models were fit to subject-specific resting-state functional magnetic resonance imaging data, and overfitting was excluded using 5-fold cross-validation. Further evaluation of the models show how balancing excitation with feedback inhibition generates an inverse relationship between -rhythms and population firing on a faster time scale and resting-state network oscillations on a slower time scale. Lastly, large-scale interactions in the model lead to the emergence of scale-free power-law spectra. Our novel findings underscore the integrative role for computational modelling to complement empirical studies.

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bioRxiv2016

Examination of memory from a first-person frame of reference provides evidence for a relationship between learning and LTP induction

Large number of correlations have been observed between behavioral markers of memory and long-term potentiation (LTP). However, there are different non-correlated findings that need explanations. Examples include a) a delay of at least thirty seconds for LTP induction after stimulation that does not match with milliseconds of time required for associative learning, and b) the deficiency of the LTP mechanism for providing a structure-function mechanism for working memory. By viewing memories as first-person inner sensations, a derived mechanism can explain various features of LTP and its mismatched findings with that of normal learning.

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bioRxiv2016

Phase-coherence classification: a new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components

Local field potentials (LFP) reflect the integrated electrophysiological activity of a large group of neurons. To minimize influence of external activity on the analysis, conventionally bipolar recordings are used to eliminate volume-conducted signals. Here we introduce a novel method, called phase-coherence classification (PCC), to separate LFP in time-frequency domain into a volume-conducted, a local incoherent and local coherent signal. The PCC allows to compute the power spectral densities of each signal and to associate each class with possible locations of electro-physiological activity. In order to test the resolution properties and accuracy of the method we generate composite and non-stationary synthetic time series with similar statistical characteristics as measured LFP. The PCC identifies volume-conducted signals with a phase threshold that is determined from probability density functions of non-phase-shifted synthetic time series. We estimate optimal PCC parameters for the analysis of beta band oscillations in LFP and apply the PCC to a test data set obtained from within the subthalamic nucleus of eight patients with Parkinsons disease (PD). We show that PCC can identify activity of multiple local clusters during a tremor episode and quantify the relative power of local and volume-conducted signals. We further analyze the electrophysiological response to an apomorphine injection during rest and show that incoherent activity in the low beta band shows a significant medication-induced decrease. We further find significant movement-induced changes on medication of the local coherent signal, which increased during an isometric hold task and decreased during phasic wrist movement. This indicates a different role of incoherent and coherent signals possibly related to physiologically different networks. This new PCC method can potentially also be applied to EEG and MEG data in order to minimize the influence of spatial leakage on power spectra and coherence estimates.

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bioRxiv2016

A mathematical approach to correlating objective spectro-temporal features of environmental sounds with their subjective perceptions.

Many studies on the subjective perception of non-linguistic sounds have focused on only a single percept, e.g. pleasantness. In the present study, we have examined three different perception-related factors to also allow us to look at any inter-relationships between them that could be related to objective features. Objective features of the NLSs in this database were calculated and subjective perceptions were recorded from participants. These two elements - objective features and subjective perceptions - were then mapped together using various statistical and mathematical techniques. So as to ground our results in a meaningful context, we chose to map a set of objective features to human percepts which had been used in previous studies of NLS perception, but which had not yet been related back to objective features or combinations thereof.

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bioRxiv2016

Impaired lexical selection with competing distractors: Evidence from left temporal and left prefrontal lesions

According to the competition account of lexical selection in word production, conceptually driven word retrieval involves the activation of a set of candidate words in left temporal cortex, and competitive selection of the intended word from this set, regulated by frontal cortical mechanisms. However, the relative contribution of these brain regions to competitive lexical selection is uncertain. In the present study, five patients with left prefrontal cortex lesions (overlapping in ventral and dorsal lateral cortex), eight patients with left lateral temporal cortex lesions (overlapping in middle temporal gyrus), and 13 matched controls performed a picture-word interference task. Distractor words were semantically related or unrelated to the picture, or the name of the picture (congruent condition). Semantic interference (related vs unrelated), tapping into competitive lexical selection, was examined. An overall semantic interference effect was observed for the control and left-temporal groups separately. The left-frontal patients did not show a reliable semantic interference effect as a group. The left-temporal patients had increased semantic interference in the error rates relative to controls. Error distribution analyses indicated that these patients had more hesitant responses for the related than for the unrelated condition. We propose that left middle temporal lesions affect the lexical activation component, making lexical selection more susceptible to errors.

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bioRxiv2016

Cause for pause before leaping to conclusions about stepping

Many neurons in parietal and prefrontal association cortex undergo gradual changes in firing rate during the formation of some perceptual decisions. These dynamics are often ramp-like increases or decreases depending on the sign and strength of the sensory evidence and are thus hypothesized to represent the accumulation of noisy samples of evidence, analogous to biased diffusion. This idea was challenged recently. An analysis of sequences of action potentials recorded from neurons in the lateral intraparietal cortex (area LIP) suggests that the spikes on single trials are explained by rates that undergo a discrete step from an intermediate rate to either a low or high rate at a random time during deliberation. The average of such steps, like the average of biased diffusion, is consistent with the ramp-like firing rates observed in LIP, but a Bayesian model comparison deemed stepping superior. Here we show that a shortcoming in the mathematical depiction of drift-diffusion led to a severe bias in the model comparison. We conclude that at present there is no compelling evidence that favors the stepping account.

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bioRxiv2016

Neuronal components of evaluating the human 1 origin of abstract shapes

Communication through visual symbols is a key aspect of human culture. However, to what extent can people distinguish between human-origin and artificial symbols, and the neuronal mechanisms underlying this process are not clear. Using fMRI we contrasted brain activity during presentation of human-created abstract shapes and random-algorithm created shapes, both sharing similar low level features.\n\nWe found that participants correctly identified most shapes as human or random. The lateral occipital complex (LOC) was the main brain region showing preference to human-made shapes, independently of task. Furthermore, LOC activity was parametrically correlated to beauty and familiarity scores of the shapes (rated following the scan). Finally, a model classifier based only on LOC activity showed human level accuracy at discriminating between human-made and randomly-made shapes.\n\nOur results highlight the sensitivity of the human brain to social and cultural cues, and point to high-order object areas as central nodes underlying this capacity.

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bioRxiv2016

Resting state functional coupling between the ascending synchronising system, limbic system and the default mode network via theta oscillations

In order to better understand dysfunction in dementia and psychiatric illnesses, the underlying neuronal systems that give rise to normal memory and cognitive processes need to be better understood. Based on electrophysiological recordings in animals, theta oscillations have been proposed as an intrinsic mechanism for the orchestration of memory functions, especially episodic and autobiographical memory. Theta oscillations are controlled by the ascending synchronising system, a set of nucleui in the pontine tegmentum and basal forebrain. At a network level, the default mode network has been shown to be responsible for episodic and autobiographical.\n\nUsing resting state fMRI data, we show using an ICA approach, seed based connectivity and dynamic causal modelling that the ascending synchronising system is coupled to the medial temporal lobe nodes including the hippocampus and parahippocampal gyrus and with the default mode network. Our results provide thus support the role of theta oscillations in memory function and coordination at a network level.\n\nHighlightsO_LIResting state functional coupling between the DMN, MTL and ascending synchronising system.\nC_LIO_LITheta oscillations may be the basis of this coupling given the role of these structures in control of theta.\nC_LIO_LITheta oscillations have been implicated in memory, cognition and predictive coding.\nC_LIO_LIDMN, MTL and ASS are implicated in Alzheimers disease.\nC_LI\n\nAbbreviations

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bioRxiv2016

CRACKING THE BARCODES OF FULLERENE-LIKE CORTICAL MICROCOLUMNS

Artificial neural systems and nervous graph theoretical analysis rely upon the stance that the neural code is endowed in logic circuits, e.g., spatio-temporal sequences of ON/OFF spiking neurons. Nevertheless, this assumption does not fully explain complex brain functions. Here we show how nervous activity, other than logic circuits, could instead depend on topological transformations and symmetry constraints occurring at the micro-level of the cortical microcolumn, i.e., the embryological, anatomical and functional basic unit of the brain. Tubular microcolumns can be flattened in guise of a fullerene-like two-dimensional lattices, equipped with about 80 nodes, standing for pyramidal neurons, where neural computations take place. We show how the countless possible combinations of activated neurons embedded in the lattice resemble a barcode. Different assemblies of firing neurons might stand for diverse codes, each one responsible for a single mental activity. A two-dimensional fullerene-like lattice not just simulates the real microcolumns microcircuitry, but also allows us to build artificial networks equipped with robustness, plasticity and fastness, because they are grounded on simple topological changes corresponding to pyramidal neurons activation.

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bioRxiv2016

A Canonical Neural Mechanism for Behavioral Variability

The ability to generate variable movements is essential for learning and adjusting complex behaviors. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioral variability is unclear. Here we combine modeling, electrophysiological and behavioral studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviors. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analyzing behavioral data, we find remarkable similarities in the babbling statistics of 5-6 month-old human infants and juveniles from three songbird species, and show that our model naturally accounts for these universal statistics.

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bioRxiv2016

L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action

Dopamine is a key neurotransmitter in reinforcement learning and action control. Recent findings suggest that these components are inherently entangled. Here, we tested if increases in dopamine tone by administration of L-DOPA upregulate deliberative \"model-based\" control of behavior or reflexive \"model-free\" control as predicted by dual-control reinforcement-learning models. Alternatively, L-DOPA may impair learning as suggested by \"value\" or \"thrift\" theories of dopamine. To this end, we employed a two-stage Markov decision-task to investigate the effect of L-DOPA (randomized cross-over) on behavioral control while brain activation was measured using fMRI. L-DOPA led to attenuated model-free control of behavior as indicated by the reduced impact of reward on choice and increased stochasticity of model-free choices. Correspondingly, in the brain, L-DOPA decreased the effect of reward while prediction-error signals were unaffected. Taken together, our results suggest that L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action.

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