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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,135 records · Page 63Linked to original sources

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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bioRxiv2016

Promiscuity of peripheral molecular biomarkers in major psychiatric disorders: a transdiagnostic systematic review

The search for biomarkers has been one of the leading endeavors in biological psychiatry; nevertheless, in spite of hundreds of publications, it has yet to make an impact in clinical practice. To study how biomarker research has progressed over the years, we performed a systematic review of the literature to evaluate (a) the most studied peripheral molecular markers in major psychiatric disorders, (b) the main features of studies in which they are proposed as biomarkers and (c) whether their patterns of variation are similar across disorders. Out of the six molecules most commonly present as keywords in articles studying plasmatic markers of schizophrenia, major depressive disorder or bipolar disorder, five (BDNF, TNF-alpha, IL-6, C-reactive protein and cortisol) were the same across the three diagnoses. An analysis of the literature on these molecules showed that, while 65% of studies were cross-sectional and 66% compared biomarker levels between patients and controls in specific disorders, only 10% presented an objective measure of diagnostic or prognostic efficacy. Meta-analyses showed that variation in the levels of these molecules was robust across studies, but also similar among disorders, suggesting them to reflect transdiagnostic systemic consequences of psychiatric illness rather than diagnostic markers. Based on this, we discuss how current publication practices have led to research fragmentation across diagnoses, and what steps can be taken in order to increase clinical translation in the field.

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bioRxiv2016

Neurons in the inferior temporal cortex of macaque monkeys are sensitive to multiple surface features from natural objects

Object surfaces contain a variety of visual features that help us to recognize them. To understand how this information is represented and processed in the brain, we prepared a set of images from natural object surfaces that maintained surface features but lacked contours. We examined spiking responses of neurons in the inferior temporal (IT) cortex of monkeys, which is a crucial structure needed for visual object recognition. About half of IT neurons responded to surface images with sharp selectivity, indicating that a significant fraction of these neurons contribute to object surface representation in a sparse manner. Responses of IT neurons were susceptible to image manipulations, including color removal, removal of luminance contrasts, and spatial structure degradation. This shows that multiple features are required for IT responses to surface images. Comparing neuronal response properties among IT, visual area 4 (V4), and primary visual cortex (V1) revealed properties of IT neurons that differed from those in the other visual processing regions. Additionally, some neuronal response properties were similar between IT and V4, but differed from those in V1, indicating that responses of IT neurons to surface images are constructed by hierarchical processing throughout the ventral visual pathway.

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bioRxiv2016

Feedback Dynamics Determine the Structureof Spike-Count Correlation in Visual Cortex

The variable responses of sensory neurons tend to be weakly correlated (spike-count correlation, rsc). This is widely thought to reflect noise in shared afferents, in which case rsc can limit the reliability of sensory coding. However, it could also be due to feedback from higher-order brain regions. Currently, the relative contribution of these sources is unknown. We addressed this by recording from populations of V1 neurons in macaques performing different discrimination tasks involving the same visual input. We found that the structure of rsc(the way rsc varied with neuronal stimulus preference) changed systematically with task instruction. Therefore, even at the earliest stage in the cortical visual hierarchy, rsc structure during task performance primarily reflects feedback dynamics. Consequently, previous proposals for how rsc constrains sensory processing need not apply. Furthermore, we show that correlations between the activity of single neurons and choice depend on feedback engaged by the task.

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bioRxiv2016

Multidimensional imbalances in cortical circuit activity in Fragile-X Syndrome mice

A leading theory for multiple brain disorders, such as schizophrenia and autism, is that they arise from developmental imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this simple 1-dimensional model is sufficiently rich to capture the set of key alterations of neural circuit processing in brain disorders. Here we used a combination of computational modeling and large-scale analysis of in vivo 2-photon Ca2+ imaging data from somatosensory cortex of wild-type and Fmr1 knock-out (KO) mice, a model of Fragile X syndrome, to test the E/I imbalance model. Our main findings were: 1) the E/I imbalance model was not rich enough to capture the alterations in neural activity statistics in Fmr1 KO mice; 2) in a computational model we found greatly varying magnitudes and directions of effects of synaptic and cellular properties on network activity; 3) evidence for opposite changes in circuit properties of Fmr1 KO mice at different stages of development; 4) a reduction in the entropy of circuit activity in young Fmr1 KO mice compared to wild-type, but an opposite increase in adult Fmr1 KO mice. These findings suggest qualitatively new strategies for developing treatments for Fragile-X Syndrome and related disorders.

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bioRxiv2016

Asymmetrical representation of the upper and lower visual fields in oculomotor maps

The striate area devoted to the lower visual field (LVF) is larger than that devoted to the upper visual field (UVF). A similar anatomical asymmetry also exists in the LGN. Here we take advantage of two experimental tasks that are known to modulate the direction and amplitude of saccades to demonstrate a visual field asymmetry in oculomotor maps. Participants made visually guided saccades. In Experiment 1, the saccade target was accompanied by a visual distractor. The distractors presence modulated the direction of saccades, and this effect was much stronger for LVF targets. In Experiment 2, the temporal gap between the offset of the fixation stimulus and the onset of the saccade target was manipulated. This manipulation modulated the amplitude of saccades and this modulation was stronger for saccades towards UVF targets. Taken together, these results suggest that the representation of both meridians and eccentricities in the LVF is compressed in oculomotor maps.

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bioRxiv2016

Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA

A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method of extracting PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high-order model across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

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bioRxiv2016

A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing

Experimental studies have revealed evidence of both parts-based and holistic representations of objects and faces in the primate visual system. However, it is still a mystery how such seemingly contradictory types of processing can coexist within a single system. Here, we propose a novel theory called mixture of sparse coding models, inspired by the formation of category-specific subregions in the inferotemporal (IT) cortex. We developed a hierarchical network that constructed a mixture of two sparse coding submodels on top of a simple Gabor analysis. The submodels were each trained with face or non-face object images, which resulted in separate representations of facial parts and object parts. Importantly, evoked neural activities were modeled by Bayesian inference, which had a top-down explaining-away effect that enabled recognition of an individual part to depend strongly on the category of the whole input. We show that this explaining-away effect was indeed crucial for the units in the face submodel to exhibit significant selectivity to face images over object images in a similar way to actual face-selective neurons in the macaque IT cortex. Furthermore, the model explained, qualitatively and quantitatively, several tuning properties to facial features found in the middle patch of face processing in IT as documented by Freiwald, Tsao, and Livingstone (2009). These included, in particular, tuning to only a small number of facial features that were often related to geometrically large parts like face outline and hair, preference and anti-preference of extreme facial features (e.g., very large/small inter-eye distance), and reduction of the gain of feature tuning for partial face stimuli compared to whole face stimuli. Thus, we hypothesize that the coding principle of facial features in the middle patch of face processing in the macaque IT cortex may be closely related to mixture of sparse coding models.

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