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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 883 records · Page 49Linked to original sources

bioRxiv2016

Effects of tDCS on motor learning and memory formation: a consensus and critical position paper

Motor skills are required for activities of daily living. Transcranial direct current stimulation (tDCS) applied in association with motor skill learning has been investigated as a tool for enhancing training effects in health and disease. Here, we review the published literature investigating whether tDCS can facilitate the acquisition and retention of motor skills and adaptation. A majority of reports focused on the application of tDCS with the anode placed over the primary motor cortex (M1) during motor skill acquisition, while some evaluated tDCS applied over the cerebellum during adaptation of existing motor skills. Work in multiple laboratories is under way to develop a mechanistic understanding of tDCS effects on different forms of learning and to optimize stimulation protocols. Efforts are required to improve reproducibility and standardization. Overall, reproducibility remains to be fully tested, effect sizes with present techniques are moderate (up to d= 0.5) (Hashemirad, Zoghi, Fitzgerald, & Jaberzadeh, 2016) and the basis of inter-individual variability in tDCS effects is incompletely understood. It is recommended that future studies explicitly state in the Methods the exploratory (hypothesis-generating) or hypothesis-driven (confirmatory) nature of the experimental designs. General research practices could be improved with prospective pre-registration of hypothesis-based investigations, more emphasis on the detailed description of methods (including all pertinent details to enable future modeling of induced current and experimental replication) and use of post-publication open data repositories. A checklist is proposed for reporting tDCS investigations in a way that can improve efforts to assess reproducibility.

Neuroscience↗

bioRxiv2016

Is the Statistic Value All We Should Care about in Neuroimaging?

Here we address an important issue that has been embedded within the neuroimaging community for a long time: the absence of effect estimates in results reporting in the literature. The statistic value itself, as a dimensionless measure, does not provide information on the biophysical interpretation of a study, and it certainly does not represent the whole picture of a study. Unfortunately, in contrast to standard practice in most scientific fields, effect (or amplitude) estimates are usually not provided in most results reporting in the current neuroimaging publications and presentations. Possible reasons underlying this general trend include: 1) lack of general awareness, 2) software limitations, 3) inaccurate estimation of the BOLD response, and 4) poor modeling due to our relatively limited understanding of FMRI signal components. However, as we discuss here, such reporting damages the reliability and interpretability of the scientific findings themselves, and there is in fact no overwhelming reason for such a practice to persist. In order to promote meaningful interpretation, cross validation, reproducibility, meta and power analyses in neuroimaging, we strongly suggest that, as part of good scientific practice, effect estimates should be reported together with their corresponding statistic values. We provide several easily adaptable recommendations for facilitating this process.

Neuroscience↗

bioRxiv2016

Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories, with which we can disentangle psychiatric conditions into basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assess and potentially diagnose psychiatric patients, and there is growing enthusiasm on RLDM and Computational Psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes as exemplified by model-based functional magnetic resonance imaging (fMRI) or electroencephalogram (EEG). However, many researchers often find the approach too technical and have difficulty adopting it for their research. Thus, there remains a critical need to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling on an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, where both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, it is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons-each with a single line of coding. Users can also extract trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational modeling approaches and investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, it is our expectation that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within their populations.

Neuroscience↗

bioRxiv2016

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

We employed a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural magnetic resonance imaging (MRI) of late-onset Alzheimers disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus and amygdala), a subcortical atrophy factor (striatum, thalamus and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (A{beta}+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in A{beta}+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, while the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared to temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Code from this manuscript is publicly available at link_to_be_added.

Neuroscience↗

bioRxiv2016

Comparison of semi-automated hippocampal subfield segmentation methods in a pediatric sample

Episodic memory function has been shown to depend critically on the hippocampus. This region is made up of a number of subfields, which differ in both cytoarchitectural features and functional roles in the mature brain. Recent neuroimaging work in children and adolescents has suggested that these regions may undergo different developmental trajectories--a fact that has important implications for how we think about learning and memory processes in these populations. Despite the growing research interest in hippocampal structure and function at the subfield level in healthy young adults, comparatively fewer studies have been carried out looking at subfield development. One barrier to studying these questions has been that manual segmentation of hippocampal subfields--considered by many to be the best available approach for defining these regions--is laborious and can be infeasible for large cross-sectional or longitudinal studies of cognitive development. Moreover, manual segmentation requires some subjectivity and is not impervious to bias or error. In a developmental sample of individuals spanning 6-30 years, we assessed the degree to which two semi-automated segmentation approaches--one approach based on Automated Segmentation of Hippocampal Subfields (ASHS) and another utilizing Advanced Normalization Tools (ANTs)--approximated manual subfield delineation on each individual by a single expert rater. Our main question was whether performance varied as a function of age group. Across several quantitative metrics, we found negligible differences in subfield validity across the child, adolescent, and adult age groups, suggesting that these methods can be reliably applied to developmental studies. We conclude that ASHS outperforms ANTs overall and is thus preferable for analyses carried out in individual subject space. However, we underscore that ANTs is also acceptable and may be well-suited for analyses requiring normalization to a single group template (e.g., voxelwise analyses across a wide age range). Previous work has supported the use of such methods in healthy young adults, as well as several special populations such as older adults and those suffering from mild cognitive impairment. Our results extend these previous findings to show that ASHS and ANTs can also be used in pediatric populations as young as six.

Neuroscience↗

bioRxiv2016

A Multi-Scale Computational Model of the effects of TMS on Motor Cortex

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

Neuroscience↗

bioRxiv2016

Specific effect of dopamine partial agonist on counterfactual learning: evidence from Gilles de la Tourette syndrome

The dopamine partial agonist aripiprazole is increasingly used to treat pathologies for which other antipsychotics are indicated because it displays fewer side effects, such as sedation and depression-like symptoms, than other dopamine receptor antagonists. Previously, we showed that aripiprazole may protect motivational function by preserving reinforcement-related signals used to sustain reward-maximization behaviour in a simple action-outcome learning task. However, the effect of aripiprazole on more cognitive facets of human reinforcement learning, such as learning from the hypothetical outcomes of alternative courses of action (i.e., counterfactual learning), is unknown.\n\nTo test the influence of aripiprazole on counterfactual learning, we administered a reinforcement-learning task that involves both direct learning from obtained outcomes and indirect learning from forgone outcomes to two groups of Gilles de la Tourette (GTS) patients, one consisting of patients who were completely unmedicated and the other consisting of patients who were receiving aripiprazole monotherapy, and to healthy subjects. We replicated a previous finding showing that aripiprazole does not affect direct learning from obtained outcomes in GTS. We also found that whereas learning performance improved in the presence of counterfactual feedback in both healthy controls and unmedicated GTS patients, this was not the case in aripiprazole-medicated GTS patients.\n\nOur results suggest that whereas aripiprazole preserves direct learning of action-outcome associations, it may impair more complex inferential processes, such as counterfactual learning, from forgone outcomes.

Neuroscience↗

bioRxiv2016

Neuronal control of the fingertips is socially configured in touchscreen smartphone users

As a common neuroscientific observation, the more a body part is used, the less variable the corresponding computations become. We here report a more complicated scenario concerning the fingertips of smartphone users. We sorted 21-days histories of touchscreen use of 57 volunteers into social and non-social categories. Sensorimotor variability was measured in a laboratory setting by simple button depressions and scalp electrodes (electroencephalogram, EEG). The ms range trial-to-trial variability in button depression was directly proportional to the number of social touches and inversely proportional to non-social touches. Variability of the early tactile somatosensory potentials was also proportional to the number of social touches, but not to non-social touches. The number of Apps and the speed of touchscreen use also reflected this variability. We suggest that smartphone use affects elementary computations even in tasks not involving a phone and that social activities uniquely reconfigure the thumb to touchscreen use.\n\nImpact StatementUnconstrained behavior on a smartphone is a powerful predictor of neuronal functions measured in the laboratory and the details of the smartphone-neuronal association challenges the established ideas of brain plasticity.

Neuroscience↗

bioRxiv2016

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cells excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaption of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.

Neuroscience↗

bioRxiv2016

The population tracking model: A simple, scalable statistical model for neural population data

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([~] 2Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, so requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex, [~] 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and surprisingly found that it first increases, then decreases during development. This statistical model opens new options for interrogating neural population data, and can bolster the use of modern large-scale in vivo Ca2+ and voltage imaging tools.

Neuroscience↗

bioRxiv2016

A causal role for right frontopolar cortex in directed, but not random, exploration.

The explore-exploit dilemma occurs anytime we must choose between exploring unknown options for information and exploiting known resources for reward. Previous work suggests that people use two different strategies to solve the explore-exploit dilemma: directed exploration driven by information seeking and random exploration driven by decision noise. Here, we show that these two strategies rely on different neural systems. Using transcranial magnetic stimulation to selectively inhibit right frontopolar cortex, we were able to selectively inhibit directed exploration while leaving random exploration intact, suggesting a causal role for right frontopolar cortex in directed, but not random, exploration.

Neuroscience↗

bioRxiv2016

Altered hippocampal interneuron activity precedes ictal onset

Although failure of GABAergic inhibition is a commonly hypothesized mechanism underlying seizure disorders, the series of events that precipitate a rapid shift from healthy to ictal activity remain unclear. Furthermore, the diversity of inhibitory interneuron populations poses a challenge for understanding local circuit interactions during seizure initiation. Using a combined optogenetic and electrophysiological approach, we examined the activity of two identified hippocampal interneuron classes during seizure induction in vivo. We identified cell type-specific differences in preictal firing patterns and input sensitivity of parvalbumin- and somatostatin-expressing interneurons. Surprisingly, the impact of both sources of inhibition remained intact throughout the preictal period and into the early ictal phase. Our findings suggest that the onset of ictal activity is not due to a failure of inhibition, but is instead associated with a decoupling of inhibitory cells from their normal relationship with the local hippocampal network.

Neuroscience↗

bioRxiv2016

Nkx2.1 regulates the proliferation and cell fate of telencephalic astrocytes during embryonic development

The homeodomain transcription factor Nkx2.1 controls cell differentiation of telencephalic GABAergic interneurons and oligodendrocytes. Here, we show that Nkx2.1 additionally regulates astrogliogenesis of the telencephalon from embryonic day (E) 14.5 to E16.5. Our work aims to identify the different mechanisms by which Nkx2.1 controls telencephalic astrogliogenesis. In Nkx2.1-/-, a drastic loss of astrocytes is observed which is not related to cell death. In vivo analysis using BrdU incorporation reveals that Nkx2.1 affects the proliferation of ventral neural stem cells that generate early astrocytes. In vitro neurosphere assays show that Nkx2.1 additionally affects the differentiation step of Nkx2.1-derived astrocytes. Chromatin immunoprecipitation and in vitro co-transfection studies of a Nkx2.1-expressing plasmid indicate that Nkx2.1 binds to the promoter of astroglial differentiation gene GFAP, and regulates its expression. Hence, Nkx2.1 controls astroglial production spatiotemporally in embryos by regulating stem cell division and specification of the contributing Nkx2.1+ precursors.

Neuroscience↗

bioRxiv2016

Neuroendocrine Modulation Sustains the C. elegans Forward Motor State

Neuromodulators shape neural circuit dynamics. Combining electron microscopy, genetics, transcriptome profiling, calcium imaging, and optogenetics, we discovered a peptidergic neuron that modulates C. elegans motor circuit dynamics. The Six/SO-family homeobox transcription factor UNC-39 governs lineage-specific neurogenesis to give rise to a neuron RID. RID bears the anatomic hallmarks of a specialized endocrine neuron: it harbors near-exclusive dense core vesicles that cluster periodically along the axon, and expresses multiple neuropeptides, including the FMRF-amide-related FLP-14. RID activity increases during forward movement. Ablating RID reduces the sustainability of forward movement, a phenotype partially recapitulated by removing FLP-14. Optogenetic depolarization of RID prolongs forward movement, an effect reduced in the absence of FLP-14. Together, these results establish the role of a neuroendocrine cell RID in sustaining a specific behavioral state in C. elegans.

Neuroscience↗

bioRxiv2016

The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core

In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use computational brain network modeling to reveal fundamental principles of how the human brain generates large scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into brain network models we construct personalized brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. Personalized, i.e. person-specific brain network modelling goes beyond correlational neuroimaging analysis and reveals the network mechanisms underlying non-invasive observations.

Neuroscience↗

bioRxiv2016

Distal axotomy enhances retrograde presynaptic excitability onto injured pyramidal neurons via trans-synaptic signaling

Injury of CNS nerve tracts remodels circuitry through dendritic spine loss and hyper-excitability, thus influencing recovery. Due to the complexity of the CNS, a mechanistic understanding of injury-induced synaptic remodeling remains unclear. Using microfluidic chambers to separate and injure distal axons, we show that axotomy causes retrograde dendritic spine loss at directly injured pyramidal neurons followed by retrograde presynaptic hyper-excitability. These remodeling events require activity at the site of injury, axon-to-soma signaling, and transcription. Similarly, directly injured corticospinal neurons in vivo also exhibit a specific increase in spiking following axon injury. Axotomy-induced hyper-excitability of cultured neurons coincides with elimination of inhibitory inputs onto injured neurons, including those formed onto dendritic spines. Netrin-1 downregulation occurs following axon injury and exogenous netrin-1 applied after injury normalizes spine density, presynaptic excitability, and inhibitory inputs at injured neurons. Our findings show that intrinsic signaling within damaged neurons regulates synaptic remodeling and involves netrin-1 signaling.

Neuroscience↗

bioRxiv2016

Patterns of individual variation in visual pathway structure and function in the sighted and blind

Many structural and functional brain alterations accompany blindness. In normally sighted people, there is correlated individual variation in some visual pathway structures. Here we examined if the changes in brain anatomy produced by blindness alter this pattern of variation. We derived eight measures of central visual pathway anatomy from an MPRAGE image of the brain from 59 sighted and 53 blind people. These measures showed highly significant differences in mean size between the sighted and blind cohorts. When we examined the measurements across individuals within each group, we found three clusters of correlated variation, with V1 surface area and pericalcarine volume linked, and independent of the thickness of V1 cortex. These two clusters were in turn relatively independent of the volumes of the optic chiasm and lateral geniculate nucleus. This same pattern of variation in visual pathway anatomy was found in the sighted and the blind. Anatomical changes within these clusters were graded by the duration of blindness, with those subjects with a post-natal onset of blindness having alterations in brain anatomy that were intermediate to those seen in the sighted and congenitally blind. Many of the blind and sighted subjects also contributed BOLD fMRI measures of cross-modal responses within visual cortex, and a diffusion tensor imaging measure of fractional anisotropy within the optic radiations and the splenium of the corpus callosum. We again found group differences between the blind and sighted in these measures. The previously identified clusters of anatomical variation were also found to be differentially related to these additional measures: across subjects, V1 cortical thickness was related to cross-modal activation, and the volume of the optic chiasm and lateral geniculate was related to fractional anisotropy in the visual pathway. Our findings show that several of the structural and functional effects of blindness may be reduced to a smaller set of dimensions. It also seems that the changes in the brain that accompany blindness are on a continuum with normal variation found in the sighted.

Neuroscience↗

bioRxiv2016

Enhanced representation of space by prefrontal neuronal ensembles and its dependence on cognitive states

Although individual neurons can be highly selective to particular stimuli and certain upcoming actions, they can provide a complex representation of stimuli and actions at the level of population. The ability to dynamically allocate neural resources is crucial for cognitive flexibility. However, it is unclear whether cognitive flexibility emerges from changes in activity at the level of individual neurons, population, or both. By applying a combination of decoding and encoding methods to simultaneously recorded neural data, we show that while maintaining their stimulus selectivity, neurons in prefrontal cortex alter their correlated activity during various cognitive states, resulting in an enhanced representation of visual space. During a task with various cognitive states, individual prefrontal neurons maintained their limited spatial sensitivity between visual encoding and saccadic target selection whereas the population selectively improved its encoding of spatial locations far from the neurons' preferred locations. This 'encoding expansion' relied on high-dimensional neural representations and was accompanied by selective reductions in noise correlation for non-preferred locations. Our results demonstrate that through recruitment of less-informative neurons and reductions of noise correlation in their activity, the representation of space by neuronal ensembles can be dynamically enhanced, and suggest that cognitive flexibility is mainly achieved by changes in neural representation at the level of population of prefrontal neurons rather than individual neurons.

Neuroscience↗