Downstream network transformations dissociate neural activity from causal functional contributions.

Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neural networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system.

The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model's object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer's disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the in-silico setup that simulates neuroplasticity improves the model's biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

When Neural Activity Fails to Reveal Causal Contributions.

Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neuronal networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.

Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell [Formula: see text] fluorescence microscopy.

Advances in high-resolution live-cell [Formula: see text] imaging enabled subcellular localization of early [Formula: see text] signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in [Formula: see text] release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic [Formula: see text] imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.

Critical slowing down as an early warning of transitions in episodes of bipolar disorder: A simulation study based on a computational model of circadian activity rhythms.

Bipolar disorder is characterized by repeated episodes of mania and depression, and can be understood as pathological complex system behaviour involving cognitive, affective and psychomotor disturbance. Accurate prediction of episode transitions in the long-term pattern of mood changes in bipolar disorder could improve the management of the disorder by providing an objective early warning of relapse. In particular, circadian activity changes measured via actigraphy may contain clinically relevant signals of imminent systemic dysregulation. In this study, we propose a mathematical index to investigate the correlation between apparently irregular circadian activity rhythms and critical transitions in episodes of bipolar disorder. Not only does the proposed index illuminate the effects of pharmacological and psychological therapies in control over the state, but it also provides a framework to understand the dynamic (or state-dependent) control strategies. Modelling analyses using our new approach suggest that key clinical goals are minimizing side effects of mood stabilizers as well as increasing the efficiency of other therapeutic strategies.

Mesoscopic model of neuronal system deficits in Multiple Sclerosis.

Multiple Sclerosis (MS) is a devastating autoimmune disease which deteriorates the connections in central nervous system (CNS) through the attacks to oligodendrocytes. Studying its origin and progression, in addition to clinical developments such as MRI brain images, cerebrospinal fluid (CSF) variation and quantitative measures of disability (EDSS), which sought to early diagnosis and efficient therapy, there is an increasing interest in developing computational models using the experimental data obtained from MS patients. From the perspective of mathematical modelling, although the origin of systemic symptoms might be attributed to cellular phenomena in microscopic level such as axonal demyelination, symptoms mainly are observed in macroscopic levels. How to fill the gap between these two levels of system modelling, however, remains as a challenge in systems biology studies. Trying to provide a conceptual framework to bridge between these two levels of modelling in systems biology, we have suggested a mesoscopic model composed of interacting neuronal population, which successfully replicates the changes in neuronal population synchrony due to MS progression.

Toward a complex system understanding of bipolar disorder: A chaotic model of abnormal circadian activity rhythms in euthymic bipolar disorder.

IMPORTANCE: In the absence of a comprehensive neural model to explain the underlying mechanisms of disturbed circadian function in bipolar disorder, mathematical modeling is a helpful tool. Here, circadian activity as a response to exogenous daily cycles is proposed to be the product of interactions between neuronal networks in cortical (cognitive processing) and subcortical (pacemaker) areas of the brain. OBJECTIVE: To investigate the dynamical aspects of the link between disturbed circadian activity rhythms and abnormalities of neurotransmitter functioning in frontal areas of the brain, we developed a novel mathematical model of a chaotic system which represents fluctuations in circadian activity in bipolar disorder as changes in the model's parameters. DESIGN, SETTING AND PARTICIPANTS: A novel map-based chaotic system was developed to capture disturbances in circadian activity across the two extreme mood states of bipolar disorder. The model uses chaos theory to characterize interplay between neurotransmitter functions and rhythm generation; it aims to illuminate key activity phenomenology in bipolar disorder, including prolonged sleep intervals, decreased total activity and attenuated amplitude of the diurnal activity rhythm. To test our new cortical-circadian mathematical model of bipolar disorder, we utilized previously collected locomotor activity data recorded from normal subjects and bipolar patients by wrist-worn actigraphs. RESULTS: All control parameters in the proposed model have an important role in replicating the different aspects of circadian activity rhythm generation in the brain. The model can successfully replicate deviations in sleep/wake time intervals corresponding to manic and depressive episodes of bipolar disorder, in which one of the excitatory or inhibitory pathways is abnormally dominant. CONCLUSIONS AND RELEVANCE: Although neuroimaging research has strongly implicated a reciprocal interaction between cortical and subcortical regions as pathogenic in bipolar disorder, this is the first model to mathematically represent this multilevel explanation of the phenomena of bipolar disorder.

Towards a complex system understanding of bipolar disorder: A map based model of a complex winnerless competition.

Bipolar disorder is characterized by repeated erratic episodes of mania and depression, which can be understood as pathological complex system behavior involving cognitive, affective and psychomotor disturbance. In order to illuminate dynamical aspects of the longitudinal course of the illness, we propose here a novel complex model based on the notion of competition between recurrent maps, which mathematically represent the dynamics of activation in excitatory (Glutamatergic) and inhibitory (GABAergic) pathways. We assume that manic and depressive states can be considered stable sub attractors of a dynamical system through which the mood trajectory moves. The model provides a theoretical framework which can account for a number of complex phenomena of bipolar disorder, including intermittent transition between the two poles of the disorder, rapid and ultra-rapid cycling of episodes and manicogenic effects of antidepressants.