Biology subjects

Yousefi, A.

Publications and source records attributed to Yousefi, A..

11 recordsLinked to original sources

bioRxiv2025

Direct observation of single intrinsically disordered proteins in solution

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) in structured proteins are integral to many biological processes including neurotransmitter regulation, microtubule regulation, and transcription. IDP/IDRs are heterogenous, existing in a conformational ensemble of various interconnected states without a definitive tertiary structure. The high dynamicity of IDPs/IDRs limits ensemble protein characterisation techniques from capturing their properties, and measurements at the single-molecule level are hampered by the necessity to label the protein or modify its microenvironment, affecting their biophysics. Consequently, our understanding of IDPs/IDRs is limited, translating to a lack of knowledge of their roles in related diseases including Alzheimers disease, Parkinsons disease, and various cancers. This work presents the first experimental observation of unmodified IDP/IDR conformational dynamics in vitro, at the single-molecule level in real time, achieved by trapping individual IDPs/IDRs in a nanoscale volume using nanoaperture optical tweezers. Our results reveal that IDPs/IDRs exhibit significantly larger conformational variations in solution compared to globular proteins of similar size, as expected. We demonstrate that phosphorylation of native tau-441 by glycogen synthase kinase 3-beta (GSK3{beta}-tau) induces compaction and reduced conformational dynamics. We further observed a disorder-to-order transition during binding of an IDR, the N-terminal region of Src-associated protein in mitosis of 68 kDa (Sam68), to G8.5 RNA. The capability of nanoaperture optical tweezers to monitor the dynamic behaviours of single, unmodified IDPs/IDRs provides a powerful approach to advance our understanding of their elusive behaviours and further decode their roles in associated diseases.

biophysics↗

bioRxiv2023

Structural Flexibility and Disassembly Kinetics of Single Ferritins using Optical Nanotweezers

Ferritin, a spherical protein shell assembled from 24 subunits, functions as an efficient iron storage and release system through its channels. Understanding how various chemicals affect the structural behaviour of ferritin is crucial for unravelling the origins of iron-related diseases in living organisms including humans. In particular, the influence of chemicals on ferritins dynamics and iron release is barely explored at the single-protein level. Here, by employing optical nanotweezers using double nanohole (DNH) structures, we examined the effect of ascorbic acid (reducing reagent) and pH on ferritins conformational dynamics. The dynamics of ferritin increased as the concentration of ascorbic acid approached saturation. At pH 2.0 ferritin exhibited significant structural fluctuations and eventually underwent a stepwise disassembly into fragments. This work, for the first time, tracked the disassembly pathway and kinetics of single ferritins in solution. We identified four critical fragments during its disassembly pathway, which are 22-mer, 12-mer, tetramer, and dimer subunits. Moreover, we presented the first single-molecule evidence of the cooperative disassembly of ferritin. Interrogating ferritins structural change in response to different chemicals holds importance for understanding their roles in iron metabolism, hence facilitating further development of medical treatments for the associated diseases.

biophysics↗

bioRxiv2022

Bayesian methods for optimizing deep brain stimulation to enhance cognitive control

ObjectiveDeep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement. ApproachWe leverage prior results showing that effective VCVS DBS engages circuits of cognitive control, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We combine this framework with an adaptive optimizer to perform a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. Main resultsUsing behavioral simulations directly derived from patient data, we show that an Upper Confidence Bound (UCB1) algorithm outperforms other optimizers (roughly 80% probability of convergence to a global optimum). SignificanceWe show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.

bioengineering↗

bioRxiv2022

Inferring Cognitive State Underlying Conflict Choices in Verbal Stroop Task Using Heterogeneous Input Discriminative-Generative Decoder Model

The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g., the response time), remains to be identified. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals response time) recorded in 10 Parkinsons disease patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy population. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural- and behavioral data simultaneously and estimate cognitive states underlying conflict and nonconflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems. HighlightsO_LIResearch highlight 1 C_LIO_LIResearch highlight 2 C_LI

neuroscience↗

bioRxiv2021

EEG-Based Focus Estimation Using Neurable's Enten Headphones and Analytics Platform

We introduce Neurables research on focus using our recently developed Enten EEG headphones. First we quantify Entens performance on standard EEG protocols, including eyes-closed alpha rhythms, auditory evoked response and the P300 event-related potential paradigm. We show that Entens performance is on-par with established industry-standard hardware. We then introduce a series of experimental tasks designed to mimic how focus might be maintained or disrupted in a real-world office setting. We show that (A) these tasks induce behavioral changes that reflect underlying changes in focus levels and (B) our proprietary algorithm detects these changes across a large number of sessions without needing to adjust the model per participant or recording session. Through manipulation of our experimental protocol, we show that our algorithm is not dependent on gross EMG artifacts and it is driven by changes in EEG. Finally, we evaluated the models performance on the same subject across several days, and show that performance remained consistent over time. Our model correctly captured 80% {+/-} 4.1% of distractions present in our experiments with statistical significance. This indicates that our model generalizes across subjects, time points, and conditions. Our findings are based on EEG data collected from 132 participants across 337 sessions and 45 different experiments.

neuroscience↗

bioRxiv2020

Does estimation methods affect on phosphorus equivalence value of phytase for layer and broiler chickens?

Two experiments were performed for evaluating calibration curve (CC) and comparing negative and positive controls (CNP) as a major method for estimating of phytase phosphorus equivalence for layer and broiler chickens. In the first and second experiments, 360 Hy-line W-36 layer hens and 525 day-old Ross-308 broiler chickens were used in a complete randomized design, respectively. Evaluated methods were setting the two regression equations for NPP-supplemented and phytase supplemented treatments with two sub-methods, include calibration curve (CC) or exclude the amount of phosphorus content of basal diet (CC-BD) in calculation, and exploring enzyme equivalency by comparing phosphorus deficient diet as an negative and supplemented diet by inorganic phosphorus sources as a positive control group (CNP). Experiment one included nine treatments (200, 300, 400 and 500 FTU/kg phytase was added to a phosphorus deficient basal diet contained 0.12% AvP, the rest four treatments were included basal diet supplemented with 0.20, 0.27, 0.35 and 0.43% AvP). Experiment two included seven treatments (a basal P deficient diet contained 0.27% AvP, and two increasing levels of AvP, 0.32 and 0.37%, and four doses of phytase 200, 300, 400 and 500 FTU/kg added to basal diet). Each treatment in the both experiments replicated five times. Results indicated that methods of estimation had a significant effect on phosphorus equivalence estimation (P<0.0001). Fitted regression equations considering P content of basal diet (CC-BD) estimated rational values than those ignore it (CC) (0.161% vs 0.365% and 0.432% vs 0.564% for 500 FTU/kg phytase for broiler chicken and layer hens, respectively) (P<0.0001). On average, among three methods used, CC method had the highest estimated values both in broiler chickens and layer hens (p<0.0001). Regardless of mathematical method, there were different significant values for different strains (layer, 0.381% and broiler, 0.179%) (P<0.0001), but not for different traits served as response criteria (P>0.05). In conclusion, the phosphorus equivalent value of enzyme varies according to the estimation methods and strain. Hence, using matrix values of enzyme for accurate feed formulation depend on a variety of circumstances and decision making requires comprehensive information.

animal behavior and cognition↗

bioRxiv2020

Analysis of Distributed Neural Synchrony through State-Space Coherence Analysis

Established methods to track the dynamics of neural representations focus at the level of individual neurons for spiking data, and individual or pair of channels for local field potentials. However, our understanding of neural function and computation has moved toward an integrative view, based upon coordinated activity of multiple neural populations across brain areas. To draw network-level inferences of brain function, we propose a new modeling framework that combines the state-space model and cross-spectral matrix estimates - this is called state-space coherence (SSCoh). We define elements of the SSCoh and derive system identification and approximate filter solution for multivariate space processes. We expand SCoh for mixed observation processes, where the observation includes different modalities of neural data including local filed potential and spiking activity. Finally, we show an application of the framework to study neural synchrony across different brain nodes of a task participant performing Stroop task under different distraction levels.

neuroscience↗

bioRxiv2020

Assessing Goodness-of-Fit in Marked-Point Process Models of Neural Population Coding via Time and Rate Rescaling

Marked-point process models have recently been used to capture the coding properties of neural populations from multi-unit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. To assess their quality, we previously proposed a goodness-of-fit technique for marked-point process models based on time-rescaling, which for a correct model, produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions. Here, we propose a set of new transformations both in time and in the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multi-dimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. We discuss properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and uniformity tests in a simulation study. Proofs for each transformation are provided in the Appendix section. We have made the MATLAB code used for the analyses in this paper publicly available through our Github repository at https://github.com/YousefiLab/Marked-PointProcess-Goodness-of-Fit

neuroscience↗

bioRxiv2019

Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

The ability to regulate emotions in the service of meeting ongoing goals and task demands is a key aspect of adaptive human behavior in our volatile social world. Consequently, difficulties in processing and responding to emotional stimuli underlie many psychiatric diseases ranging from depression to anxiety, the common thread being effects on behavior. Behavior, which is made up of shifting, difficult to measure hidden states such as attention and emotion reactivity, is a product of integrating external input and latent mental processes. Directly measuring, and differentiating, separable hidden cognitive, emotional, and attentional states contributing to emotion conflict resolution, however, is challenging, particularly when only using task-relevant behavioral measures such as reaction time. State-space representations are a powerful method for investigating hidden states underlying complex systems. Using state-space modeling of behavior, we identified relevant hidden cognitive states and predicted behavior in a standardized emotion regulation task. After identifying and validating models which best fit the behavior and narrowing our focus to one model, we used targeted intracranial stimulation of the emotion regulation-relevant neurocircuitry, including prefrontal structures and the amygdala, to causally modulate separable states. Finally, we focused on this one validated state-space model to perform real-time, bidirectional closed-loop adaptive stimulation in a subset of participants. These approaches enable an improved understanding of how to sample and understand emotional processing in a way which could be leveraged in neuromodulatory therapy for disorders of emotional regulation.

neuroscience↗

bioRxiv2018

Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models

There is an increasing demand for a computationally efficient and accurate point process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the point process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general point-process filter problem when the conditional intensity of a cells spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of point process observation called marked point-process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional point-process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rats position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% HPD coverage performance metrics. Though the marked-point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.

bioinformatics↗

bioRxiv2018

COMPASS: An Open-Source, General-Purpose Software Toolkit for Computational Psychiatry

Mathematical modeling of behavior during psychophysical tasks, referred to as \"computational psychiatry\", could greatly improve our understanding of mental disorders. One barrier to broader adoption of computational methods is that they often require advanced programming skills. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows the user to efficiently fit of a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data, and can be tested using goodness-of-fit methods. Here, we demonstrate that COMPASS can replicate two computational behavior analyses from different groups. COMPASS replicates and, in one case, slightly improves on the original modeling results. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.

bioengineering↗