Biology subjects

Ghazizadeh, A.

Publications and source records attributed to Ghazizadeh, A..

15 recordsLinked to original sources

bioRxiv2024

Spatially Enhanced Processing for Valuable Objects in Prefrontal Cortex Neurons During Efficient Search

It is recently shown that objects with long-term reward associations can be efficiently located during visual search. The neural mechanism for valuable object pop-out is unknown. In this work, we recorded neuronal responses in the ventrolateral prefrontal cortex (vlPFC) with known roles in visual search and reward processing in macaques while monkeys engaged in efficient vs inefficient visual search for high-value fractal objects (targets). Behavioral results and modeling using multi-alternative attention-modulated drift-diffusion (MADD) indicated that efficient search was concurrent with enhanced processing for peripheral objects. Notably, neural results showed response amplification and receptive field widening to peripherally presented targets in vlPFC during visual search. Both neural effects predict higher target detection and were found to be correlated with it. Our results suggest that value-driven efficient search independent of low-level visual features arises from reward-induced spatial processing enhancement of peripheral valuable objects.

neuroscience↗

bioRxiv2024

Prefrontal cortex temporally multiplexes slow and fast dynamics in value learning and memory

Previous studies have revealed segregated circuitries in basal ganglia for fast learning that enables value adaptability and slow forgetting which underlies stable value memories. However, the mechanisms mediating the conflict between value adaptability vs stability remain unknown. Using a reinforcement learning paradigm involving a brief value reversal for objects with previously stable values, we predicted and confirmed a novel behavioral manifestation of the conflict between adaptability vs stability namely the spontaneous recovery of old values in macaque monkeys. Furthermore, we found that individual neurons in ventrolateral prefrontal cortex (vlPFC) temporally multiplexed slow and fast processes in their early and late responses to objects. The local field potential in vlPFC also reflected the two-rate system. These findings implicate vlPFC as a plexus for the interactions between adaptability vs stability in reinforcement learning and suggest spontaneous recovery of past values caused by a two-rate system to mediate relapse to old habits.

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bioRxiv2023

Prefrontal cortex signals value category while basal ganglia represent learned values in value learning

Value learning can be variable for objects with the exact same reward but the underlying neural mechanism for such variability is not known. We have addressed this question by recording single-unit activity in the prefrontal cortex (PFC) and substantia nigra reticulata (SNr), two key nodes in cortex-basal ganglia circuitry with crucial roles in value learning, as macaque monkeys learned to associate novel objects with either high or low rewards. Estimating the trial by trial learned values based on choice performance, revealed stark differences between learned values across objects with the same reward outcome. Importantly, while PFC neurons rapidly learned to differentiate objects based on their value category, the firing in SNr correlated with the variability in learned values within a value category. Our results suggest that the variation in objects learned values is more likely to be a readout of SNr firing while PFC may provide a top-down teaching signal to basal ganglia to demarcate the value categories.

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bioRxiv2023

Robust memory of face moral values is encoded in the human caudate tail: A simultaneous EEG-fMRI study

Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a persons face are processed and stored in the brain. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people, are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole brain analysis revealed any significant activation in cortical areas in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. These results identify CDt as the main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.

neuroscience↗

bioRxiv2023

Value-based search efficiency is encoded in substantia nigra reticulata firing rate, spiking irregularity and local field potential

Recent results show that valuable objects can pop-out in visual search yet its neural mechanisms remain unexplored. Given the role of substantia nigra reticulata (SNr) in object value memory and control of gaze, we recorded its single unit activity while male macaque monkeys engaged in efficient or inefficient search for a valuable target object among low-value objects. Results showed that efficient search was concurrent with stronger inhibition and higher spiking irregularity in target present (TP) compared to target absent (TA) trials in SNr. Importantly, the firing rate differentiation of TP and TA trials happened within [~]100ms of display onset and its magnitude was significantly correlated with the search times and slopes (aka search efficiency). Time-frequency analyses of local field potential (LFP) after display onset revealed significant modulations of gamma band power with search efficiency. The greater reduction of SNr firing in TP trials in efficient search can create a stronger disinhibition of downstream superior colliculus which in turn can facilitate saccade to obtain valuable targets in competitive environments. Significant statementMost times we have to find a few relevant or highly valued objects among many objects that surround us. When our target objects are not distinct from their surroundings based on low-level features, searching for them becomes tedious and slow. Nevertheless, recent findings show that valuable objects can be found efficiently and fast if they have been repeatedly paired with reward. Our results show that the rate and pattern of spiking as well as local voltage fluctuations in the basal ganglia output which is known to control attention and saccade correlate with such value-driven search efficiency. Thus, in addition to reward learning, basal ganglia can have a role in skillful interactions with and rapid detection of rewarding objects.

neuroscience↗

bioRxiv2023

Cortical and subcortical substrates of minutes and days-long object value memory in humans

Obtaining valuable objects motivates many of our daily decisions. However, the neural underpinnings of object processing based on human value memory are not yet fully understood. Here, we used whole-brain fMRI to examine activations due to value memory as participants passively viewed objects before, minutes after, and 1-70 days following value training. Significant value memory for objects was evident in the behavioral performance, which nevertheless faded over the days following training. Minutes after training, the occipital, ventral temporal, interparietal, and frontal areas showed strong value discrimination. Days after training, activation in the frontal, temporal, and occipital regions decreased, whereas the parietal areas showed sustained activation. In addition, days-long value responses emerged in certain subcortical regions, including the caudate, ventral striatum, and thalamus. Resting-state analysis revealed that these subcortical areas were functionally connected. Furthermore, the activation in the thalamo-striatal cluster was positively correlated with participants performance in days-long value memory. These findings shed light on the neural basis of value memory in humans with implications for object habit formation and cross-species comparisons.

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bioRxiv2023

Prefrontal Cortex Encodes Value Pop-out in Visual Search

Recent evidence shows that long-term object value association can lead to efficient visual search. However, the neural mechanism of this value pop-out has yet to be understood. Given the known role of the ventrolateral prefrontal cortex (vlPFC) in visual search and value memory, we recorded its single-unit activity (n=526) in two macaque monkeys while they engaged in the value-driven search. Monkeys had to determine whether a high-value target was present within a variable number of low-value objects. Interestingly, differential neural firing, as well as gamma-band power, indicated the presence of a target within [~]150ms of display onset. This differential activity was negatively correlated with search time and became less display size-dependent for more efficient searches. On the other hand, neural firing and its variability were higher in inefficient searches. These findings reveal the neural code within vlPFC for rapid detection of valuable targets, which can be crucial for animals faced with competition. Significance StatementSearch for rewarding objects is ubiquitous and crucial for animals and humans alike. Up until recently, it was thought that visual search for valuable targets that were otherwise not distinct by low-level features should be serial and slow. Contrary to this belief, we showed that given sufficient reward training, valuable objects can be found efficiently in search suggesting a value pop-out neural mechanism. Importantly, we reveal the neural activity in the prefrontal cortex (PFC) to be predictive of the degree of value-driven search efficiency. Given the role of PFC in object value memory, these results show how PFC can translate such memories to emulate the parallel processing of visual information independent of low-level visual features.

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bioRxiv2021

Gustatory cortex is involved in evidence accumulation during food choice

Food choice is one of the most fundamental and most frequent value-based decisions for all animals including humans. However, the neural circuitry involved in food-based decisions is only recently being addressed. Given the relatively fast dynamics of decision formation, EEG-informed fMRI analysis is highly beneficial for localizing this circuitry in humans. Here by using the EEG correlates of evidence accumulation in a simultaneously recorded EEG-fMRI dataset, we found a significant role for the right temporal-parietal operculum (PO) and medial insula including gustatory cortex (GC) in binary choice between food items. These activations were uncovered by using the "EEG energy" (power 2) as the BOLD regressor and were missed if conventional analysis with the EEG signal itself were to be used, in agreement with theoretical predictions for EEG and BOLD relations. No significant positive correlations were found with higher powers of EEG (powers 3 or 4) pointing to specificity and sufficiency of EEG energy as the main correlate of the BOLD response. This finding extends the role of cortical areas traditionally involved in palatability processing to value-based decision making and offers the "EEG energy" as a key regressor of BOLD response in simultaneous EEG-fMRI designs.

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bioRxiv2021

Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate responses (UVR) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of weak classifiers (i.e. single voxels) in higher dimensions. We propose instead that real multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate responses (sMVR). Surprisingly, analysis of conditions with robust multivariate responses (MVR) revealed by MVPA failed to show significant sMVR in two species (humans and macaques). Further analysis showed that while both MVR and sMVR can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.

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bioRxiv2021

Feedback alignment with weight normalization can provide a biologically plausible mechanism for learning

Understanding the mechanisms by which plasticity in millions of synapses in the brain is orchestrated to achieve behavioral and cognitive goals is a fundamental quest in neuroscience. In this regard, insights from learning methods in artificial neural networks (ANNs) and in particular supervised learning using backpropagation (BP) seem inspiring. However, the implementation of BP requires exact matching between forward and backward weights, which is unrealistic given the known connectivity pattern in the brain (known as "weight transport problem"). Notably, it has been shown that under certain conditions, error BackPropagation Through Arbitrary Weights (BP-TAW) can lead to a partial alignment between forward and backward weights (weight alignment or WA). This learning algorithm, which is also known as feedback alignment (FA), can result in surprisingly good degrees of accuracy in simple classification tasks. However, the underlying mechanisms and mathematical basis of WA are not thoroughly understood. In this work, we show that the occurrence of WA is governed by statistical properties of the output and error signals of neurons, such as autocorrelation and cross-correlation, and can happen even in the absence of learning or reduction of the loss function. Moreover, we show that WA can be improved significantly by limiting the norm of input weights to neurons and that such a weight normalization (WN) method can improve the classification accuracy of BP-TAW. The findings presented can be used to further improve the performance of BP-TAW and open new ways for exploring possible learning mechanisms in biological neural networks without exact matching between forward and backward weights.

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bioRxiv2021

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contribution of within trial spike irregularity (n{Psi}) and trial to trial rate variability (nRV) to FF reduction has remained elusive. Here, we introduce a principled approach for accurate estimation of variability components for a doubly stochastic point process which unlike previous methods allows for a time varying n{Psi} (aka {phi}). Notably, analysis across multiple subcortical and cortical areas showed across the board reduction in rate variability. However, unlike what was previously thought, spiking irregularity was not constant in time and was even enhanced in some regions abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying n{Psi} arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.

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bioRxiv2020

Optimal Binning of Peri-Event Time Histograms Using Akaike Information Criterion

Peri-event time histograms (PETH) are widely used to study correlations between experimental events and neuronal firing. The accuracy of firing rate estimate using a PETH depends on the choice of binsize. We show that the optimal binsize for a PETH depends on factors such as the number of trials and the temporal dynamics of the firing rate. These factors argue against the use of a one-size-fits-all binsize when making PETHs for an inhomogeneous population of neurons. Here we propose a binsize selection method by adapting the Akaike Information Criterion (AIC). Simulations show that optimal binsizes estimated by AIC closely match the optimal binsizes using mean squared error (MSE). Furthermore, using real data, we find that optimal binning improves detection of responses and their dynamics. Together our analysis strongly supports optimal binning of PETHs and proposes a computationally efficient method for this optimization based on AIC approach to model selection.

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bioRxiv2019

Object novelty and value coding segregate across functionally connected brain networks

Novel and valuable objects are motivationally attractive for animals including primates. However, little is known about how novelty and value processing is organized across the brain. We used fMRI in macaques to map brain activity to fractal patterns varying in either novelty or value dimensions in the context of functionally connected brain networks determined at rest. Results show unique combinations of novelty and value coding across the brain networks. Networks in the ventral temporal cortex and in the parietal cortex showed preferential coding of novelty and value dimensions, respectively, while a wider network composed of temporal and prefrontal areas (TP network), along with functionally connected portions of the striatum, amygdala, and claustrum, responded to both dimensions with similar activation dynamics. Our results support emergence of a common currency signal in the TP network that may underlie the common attitudes toward novel and valuable objects.

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bioRxiv2018

A fully automated spike sorting algorithm using t-distributed neighbor embedding and density based clustering

In this study, a new spike sorting method was developed based on a combination of two methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Parameters of both methods were simultaneously optimized using a Genetic Algorithm (GA) using a simulated dataset containing 2 to 20 simultaneously recorded neurons. The performance of this method was evaluated using both the simulated dataset as well as real multichannel electrophysiological data. The results indicated that our fully automated algorithm using t-SNE-DBSCAN outperforms other state-of-the-art algorithms and human experts in spike sorting especially when there are a large number of simultaneously recorded units. Our algorithm also determines the noise waveforms and has an overall high sensitivity, precision and accuracy for correctly classifying waveforms belonging to each neuron (all >90%) without the need for manual corrections afterwards. Our method can be a crucial part of the analysis pipeline in particular when manual sorting of units is becoming prohibitive due to the sheer number of recorded neurons per session.

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bioRxiv2018

Drift-diffusion explains response variability and capacity for tracking objects

1.Being able to track objects that surround us is key for planning actions in dynamic environments. However, rigorous cognitive models for tracking of one or more objects are currently lacking. In this study, we asked human subjects to judge the time to contact (TTC) a finish line for one or two objects that became invisible shortly after moving. We showed that the pattern of subject responses had an error variance best explained by an inverse Gaussian distribution and consistent with the output of a biased drift-diffusion model. Furthermore, we demonstrated that the pattern of errors made when tracking two objects showed a level of dependence that was consistent with subjects using a single decision variable for reporting the TTC for two objects. This finding reveals a serious limitation in the capacity for tracking multiple objects resulting in error propagation between objects. Apart from explaining our own data, our approach helps interpret previous findings such as asymmetric interference when tracking multiple objects.

neuroscience↗