Shared Neural Activity But Distinct Neural Dynamics for Cognitive Control in Monkey Prefrontal and Parietal Cortex.

To better understand how prefrontal networks mediate forms of cognitive control disrupted in schizophrenia, we translated a variant of the AX continuous performance task that measures specific deficits in the human disease to 2 male monkeys and recorded neurons in PFC and parietal cortex during task performance. In the task, contextual information instructed by cue stimuli determines the response required to a subsequent probe stimulus. We found parietal neurons encoding the behavioral context instructed by cues that exhibited nearly identical activity to their prefrontal counterparts (Blackman et al., 2016). This neural population switched their preference for stimuli over the course of the trial depending on whether the stimuli signaled the need to engage cognitive control to override a prepotent response. Cues evoked visual responses that appeared in parietal neurons first, whereas population activity encoding contextual information instructed by cues was stronger and more persistent in PFC. Increasing cognitive control demand biased the representation of contextual information toward the PFC and augmented the temporal correlation of task-defined information encoded by neurons in the two areas. Oscillatory dynamics in local field potentials differed between cortical areas and carried as much information about task conditions as spike rates. We found that, at the single-neuron level, patterns of activity evoked by the task were nearly identical between the two cortical areas. Nonetheless, distinct population dynamics in PFC and parietal cortex were evident. suggesting differential contributions to cognitive control.SIGNIFICANCE STATEMENT We recorded neural activity in PFC and parietal cortex of monkeys performing a task that measures cognitive control deficits in schizophrenia. This allowed us to characterize computations performed by neurons in the two areas to support forms of cognitive control disrupted in the disease. Subpopulations of neurons in the two areas exhibited parallel modulations in firing rate; and as a result, all patterns of task-evoked activity were distributed between PFC and parietal cortex. This included the presence in both cortical areas of neurons reflecting proactive and reactive cognitive control dissociated from stimuli or responses in the task. However, differences in the timing, strength, synchrony, and correlation of information encoded by neural activity were evident, indicating differential contributions to cognitive control.

Comparing the functional neuroanatomy of proactive and reactive control between patients with schizophrenia and healthy controls.

Cognitive control deficits are associated with impaired executive functioning in schizophrenia. The Dual Mechanisms of Control framework suggests that proactive control requires sustained dorsolateral prefrontal activity, whereas reactive control marshals a larger network. However, primate studies suggest these processes are maintained by dual-encoding regions. To distinguish between these theories, we compared the distinctiveness of proactive and reactive control functional neuroanatomy. In a reanalysis of data from a previous study, 47 adults with schizophrenia and 56 controls completed the Dot Pattern Expectancy task during an fMRI scan examining proactive and reactive control in frontoparietal and medial temporal regions. Areas suggesting specialized control or between-group differences were tested for association with symptoms and task performance. Elastic net models additionally explored these areas' predictive abilities regarding performance. Most regions were active in both reactive and proactive control. However, evidence of specialized proactive control was found in the left middle and superior frontal gyri. Control participants showed greater proactive control in the left middle and right inferior frontal gyri. Elastic net models moderately predicted task performance and implicated various frontal gyri regions in control participants, with additional involvement of anterior cingulate and posterior parietal regions for reactive control. Elastic nets for patient participants implicated the inferior and superior frontal gyri, and posterior parietal lobe. Specialized cognitive control was unassociated with either performance or schizophrenia symptomatology. Future work is needed to clarify the distinctiveness of proactive and reactive control, and its role in executive deficits in severe psychopathology.

Differential Roles of Mediodorsal Nucleus of the Thalamus and Prefrontal Cortex in Decision-Making and State Representation in a Cognitive Control Task Measuring Deficits in Schizophrenia.

The mediodorsal nucleus of the thalamus (MD) is reciprocally connected with the prefrontal cortex (PFC), and although the MD has been implicated in a range of PFC-dependent cognitive functions (Watanabe and Funahashi, 2012; Mitchell and Chakraborty, 2013; Parnaudeau et al., 2018), little is known about how MD neurons in the primate participate specifically in cognitive control, a capability that reflects the ability to use contextual information (such as a rule) to modify responses to environmental stimuli. To learn how the MD-PFC thalamocortical network is engaged to mediate forms of cognitive control that are selectively disrupted in schizophrenia, we trained male monkeys to perform a variant of the AX continuous performance task, which reliably measures cognitive control deficits in patients (Henderson et al., 2012) and used linear multielectrode arrays to record neural activity in the MD and PFC simultaneously. We found that the two structures made clearly different contributions to distributed processing for cognitive control: MD neurons were specialized for decision-making and response selection, whereas prefrontal neurons were specialized to preferentially encode the environmental state on which the decision was based. In addition, we observed that functional coupling between MD and PFC was strongest when the decision as to which of the two responses in the task to execute was being made. These findings delineate unique contributions of MD and PFC to distributed processing for cognitive control and characterized neural dynamics in this network associated with normative cognitive control performance.SIGNIFICANCE STATEMENT Cognitive control is fundamental to healthy human executive functioning (Miller and Cohen, 2001) and deficits in patients with schizophrenia relate to decreased functional activation of the MD thalamus and the prefrontal cortex (Minzenberg et al., 2009), which are reciprocally linked (Goldman-Rakic and Porrino, 1985; Xiao et al., 2009). We carry out simultaneous neural recordings in the MD and PFC while monkeys perform a cognitive control task translated from patients with schizophrenia to relate thalamocortical dynamics to cognitive control performance. Our data suggest that state representation and decision-making computations for cognitive control are preferentially performed by PFC and MD, respectively. This suggests experiments to parse decision-making and state representation deficits in patients while providing novel computational targets for future therapies.

Monkey Prefrontal Neurons Reflect Logical Operations for Cognitive Control in a Variant of the AX Continuous Performance Task (AX-CPT).

Cognitive control is the ability to modify the behavioral response to a stimulus based on internal representations of goals or rules. We sought to characterize neural mechanisms in prefrontal cortex associated with cognitive control in a context that would maximize the potential for future translational relevance to human neuropsychiatric disease. To that end, we trained monkeys to perform a dot-pattern variant of the AX continuous performance task that is used to measure cognitive control impairment in patients with schizophrenia (MacDonald, 2008;Jones et al., 2010). Here we describe how information processing for cognitive control in this task is related to neural activity patterns in prefrontal cortex of monkeys, to advance our understanding of how behavioral flexibility is implemented by prefrontal neurons in general, and to model neural signals in the healthy brain that may be disrupted to produce cognitive control deficits in schizophrenia. We found that the neural representation of stimuli in prefrontal cortex is strongly biased toward stimuli that inhibit prepotent or automatic responses. We also found that population signals encoding different stimuli were modulated to overlap in time specifically in the case that information from multiple stimuli had to be integrated to select a conditional response. Finally, population signals relating to the motor response were biased toward less frequent and therefore less automatic actions. These data relate neuronal activity patterns in prefrontal cortex to logical information processing operations required for cognitive control, and they characterize neural events that may be disrupted in schizophrenia. SIGNIFICANCE STATEMENT: Functional imaging studies have demonstrated that cognitive control deficits in schizophrenia are associated with reduced activation of the dorsolateral prefrontal cortex (MacDonald et al., 2005). However, these data do not reveal how the disease has disrupted the function of prefrontal neurons to produce the observed deficits in cognitive control. Relating cognitive control to neurophysiological signals at a cellular level in prefrontal cortex is a necessary first step toward understanding how disruption of these signals could lead to cognitive control failure in neuropsychiatric disease. To that end, we translated a task that measures cognitive control deficits in patients with schizophrenia to monkeys and describe here how neural signals in prefrontal cortex relate to performance.

Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error.

In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction errors from the previous state predictions as inputs to the hidden units of the network. We show that the time course of sequence prediction errors generated by the model closely matched the activity time courses of populations of neurons in macaque prefrontal cortex. Hidden units in the model responded to combinations of task variables and exhibited sensitivity to changing stimulus probability in ways that closely resembled monkey prefrontal neurons. Moreover, the model generated prolonged response times to infrequent, unexpected events as did monkeys. The results suggest that prefrontal cortex may generate internal models of the temporal structure of the world even during tasks that do not explicitly depend on temporal expectation, using a sequence prediction error minimization learning rule to do so. As such, the SPEL model provides a unified, general-purpose theoretical framework for modeling the lateral prefrontal cortex.

A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia.

Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and 0-lag spike synchrony deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence oscillatory spike synchrony to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. These findings strongly parallel dynamic modulation of 0-lag spike synchrony we observed between neurons in monkey prefrontal cortex during behavior, as well as the suppression of this 0-lag spiking by administration of NMDAR antagonists. As such, our cortical network model provides a plausible mechanism explaining the link between NMDAR synaptic and 0-lag spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

Schizophrenia is a long-term mental health condition that can cause a person to see, hear or believe things that are not real. Although researchers do not fully understand the causes of schizophrenia, it is known to disrupt synapses, which connect neurons in the brain to form circuits that carry out a specific function when activated. This disruption alters the pattern of activity among the neurons, distorting the way that information is processed and leading to symptoms. Development of schizophrenia is thought to be due to interactions between many factors, including genetic makeup, changes in how the brain matures during development, and environmental stress. Despite animal studies revealing how neural circuits can fail at the level of individual cells, it remains difficult to predict or understand the complex ways that this damage affects advanced brain functions. Previous research in monkeys showed that mimicking schizophrenia using a drug that blocks a particular type of synapse prevented neurons from coordinating their activity. However, this did not address how synaptic and cellular changes lead to disrupted neural circuits. To better understand this, Crowe et al. developed a computational model of neural circuits to study how they respond to synapse disruption. To replicate the brain, the model consisted of two types of neurons ­ those that activate connecting cells in response to received signals and those that suppress them. This model could replicate the complex network behavior that causes brain cells to respond to sensory inputs. Increasing the strength of inputs to the network caused it to switch from a state in which the cells fired independently to one where the cells fired at the same time. As was previously seen in monkeys, blocking a particular type of synapse thought to be involved in schizophrenia prevented the cells from coordinating their signaling. The findings suggest that schizophrenia-causing factors can reduce the ability of neurons to fire at the same instant. Disrupting this process could lead to weaker and fewer synapses forming during brain development or loss of synapses in adults. If that is the case, and scientists can understand how factors combine to trigger this process, the mechanism of coordinated activity failure revealed by the model could help identify treatments that prevent or reverse the synapse disruption seen in schizophrenia.

Executive control over cognition: stronger and earlier rule-based modulation of spatial category signals in prefrontal cortex relative to parietal cortex.

Human cognition is characterized by flexibility, the ability to select not only which action but which cognitive process to engage to best achieve the current behavioral objective. The ability to tailor information processing in the brain to rules, goals, or context is typically referred to as executive control, and although there is consensus that prefrontal cortex is importantly involved, at present we have an incomplete understanding of how computational flexibility is implemented at the level of prefrontal neurons and networks. To better understand the neural mechanisms of computational flexibility, we simultaneously recorded the electrical activity of groups of single neurons within prefrontal and posterior parietal cortex of monkeys performing a task that required executive control of spatial cognitive processing. In this task, monkeys applied different spatial categorization rules to reassign the same set of visual stimuli to alternative categories on a trial-by-trial basis. We found that single neurons were activated to represent spatially defined categories in a manner that was rule dependent, providing a physiological signature of a cognitive process that was implemented under executive control. We found also that neural signals coding rule-dependent categories were distributed between the parietal and prefrontal cortex--however, not equally. Rule-dependent category signals were stronger, more powerfully modulated by the rule, and earlier to emerge in prefrontal cortex relative to parietal cortex. This suggests that prefrontal cortex may initiate the switch in neural representation at a network level that is important for computational flexibility.

Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity.

Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.

Prefrontal cortex.

The prefrontal cortex is a well-studied but, in terms of understanding what it is for, deeply divisive part of the brain located at the front of the head. Perhaps the least controversial feature of the prefrontal cortex is its complexity. The prefrontal cortex is anatomically, functionally, and computationally complex. It is anatomically complex, containing a number of subregions each sending and receiving projections to a unique set of other cortical and subcortical areas. This interconnectivity presents a serious challenge to efforts to localize function to prefrontal cortex, because it can seem as though information flows everywhere all at once in prefrontal networks. Perhaps as a result, prefrontal cortex is also computationally complex: working memory, abstraction, sensory attention, value-based decision making, planning, and motor control are all functions that have been attributed to the prefrontal cortex. This diversity of functions is likely to reflect the diversity of brain regions that prefrontal cortex communicates with while carrying out the computations it performs to influence behavior.

Disparate insults relevant to schizophrenia converge on impaired spike synchrony and weaker synaptic interactions in prefrontal local circuits.

Schizophrenia results from hundreds of known causes, including genetic, environmental, and developmental insults that cooperatively increase risk of developing the disease. In spite of the diversity of causal factors, schizophrenia presents with a core set of symptoms and brain abnormalities (both structural and functional) that particularly impact the prefrontal cortex. This suggests that many different causal factors leading to schizophrenia may cause prefrontal neurons and circuits to fail in fundamentally similar ways. The nature of convergent malfunctions in prefrontal circuits at the cell and synaptic levels leading to schizophrenia are not known. Here, we apply convergence-guided search to identify core pathological changes in the functional properties of prefrontal circuits that lie downstream of mechanistically distinct insults relevant to the disease. We compare the impacts of blocking NMDA receptors in monkeys and deleting a schizophrenia risk gene in mice on activity timing and effective communication in prefrontal local circuits. Although these manipulations operate through distinct molecular pathways and biological mechanisms, we found they produced convergent pathophysiological effects on prefrontal local circuits. Both manipulations reduced the frequency of synchronous (0-lag) spiking between prefrontal neurons and weakened functional interactions between prefrontal neurons at monosynaptic lags as measured by information transfer between the neurons. The two observations may be related, as reduction in synchronous spiking between prefrontal neurons would be expected to weaken synaptic connections between them via spike-timing-dependent synaptic plasticity. These data suggest that the link between spike timing and synaptic connectivity could comprise the functional vulnerability that multiple risk factors exploit to produce disease.

Rapid sequences of population activity patterns dynamically encode task-critical spatial information in parietal cortex.

We characterized the temporal dynamics of population activity in parietal cortex of monkeys as they solved a spatial cognitive problem posed by an object construction task. We applied pattern classification techniques to characterize patterns of activity coding object-centered side, a task-defined variable specifying whether an object component was located on the left or right side of a reference object, regardless of its retinocentric position. During a period in which the value of object-centered side, as defined by task events, remained constant, parietal cortex represented this variable using a dynamic neural code by activating neurons with the same spatial preference in rapid succession so that the pattern of active neurons changed dramatically while the spatial information they collectively encoded remained stable. Furthermore, if the neurons shared the same spatial preference, then their pretrial activity (measured before objects were shown) was correlated to a degree that scaled as a positive linear function of how close together in time the neurons would be activated later in the trial. Finally, we found that while parietal cortex represented task-critical spatial information using a dynamic neural code, it simultaneously represented task-irrelevant spatial information using a stationary neural code. These data demonstrate that dynamic spatial representations exist in parietal cortex, provide novel insight into the synaptic mechanisms that generate them, and suggest they may preferentially encode task-critical spatial information.

If I do A, B will happen: Dissecting circuits detecting causal relations between actions and outcomes in marmoset prefrontal cortex.

In this issue of Neuron, Duan et al. (2021) use pharmacological manipulation to reveal opposing influences of anterior cingulate and orbitofrontal cortex of marmosets on decisions that are based on action-outcome associations.

A View from the Top: Prefrontal Control of Object Recognition.

In this issue of Neuron, Kar and DiCarlo (2021) demonstrate that feedback from ventrolateral prefrontal cortex (VLPFC) to inferotemporal cortex (IT) is required for object recognition. They show that inactivating VLPFC selectively degrades object recognition performance and population encoding of object identity in IT.

Understanding the parietal lobe syndrome from a neurophysiological and evolutionary perspective.

In human and nonhuman primates parietal cortex is formed by a multiplicity of areas. For those of the superior parietal lobule (SPL) there exists a certain homology between man and macaques. As a consequence, optic ataxia, a disturbed visual control of hand reaching, has similar features in man and monkeys. Establishing such correspondence has proven difficult for the areas of the inferior parietal lobule (IPL). This difficulty depends on many factors. First, no physiological information is available in man on the dynamic properties of cells in the IPL. Second, the number of IPL areas identified in the monkey is paradoxically higher than that so far described in man, although this issue will probably be reconsidered in future years, thanks to comparative imaging studies. Third, the consequences of parietal lesions in monkeys do not always match those observed in humans. This is another paradox if one considers that, in certain cases, the functional properties of neurons in the monkey's IPL would predict the presence of behavioral skills, such as construction capacity, that however do not seem to emerge in the wild. Therefore, constructional apraxia, which is well characterized in man, has never been described in monkeys and apes. Finally, only certain aspects, i.e. hand directional hypokinesia and gaze apraxia (Balint's psychic paralysis of gaze), of the multifaceted syndrome hemispatial neglect have been described in monkeys. These similarities, differences and paradoxes, among many others, make the study of the evolution and function of parietal cortex a challenging case.

Cognitive Control Errors in Nonhuman Primates Resembling Those in Schizophrenia Reflect Opposing Effects of NMDA Receptor Blockade on Causal Interactions Between Cells and Circuits in Prefrontal and Parietal Cortices.

BACKGROUND: The causal biology underlying schizophrenia is not well understood, but it is likely to involve a malfunction in how neurons adjust synaptic connections in response to patterns of activity in networks. We examined statistical dependencies between neural signals at the cell, local circuit, and distributed network levels in prefrontal and parietal cortices of monkeys performing a variant of the AX continuous performance task paradigm. We then quantified changes in the pattern of neural interactions across levels of scale following NMDA receptor (NMDAR) blockade and related these changes to a pattern of cognitive control errors closely matching the performance of patients with schizophrenia. METHODS: We recorded the spiking activity of 1762 neurons along with local field potentials at multiple electrode sites in prefrontal and parietal cortices concurrently, and we generated binary time series indicating the presence or absence of spikes in single neurons or local field potential power above or below a threshold. We then applied causal discovery analysis to the time series to detect statistical dependencies between the signals (causal interactions) and compared the pattern of these interactions before and after NMDAR blockade. RESULTS: Global blockade of NMDAR produced distinctive and frequently opposite changes in neural interactions at the cell, local circuit, and network levels in prefrontal and parietal cortices. Cognitive control errors were associated with decreased interactions at the cell level and with opposite changes at the network level in prefrontal and parietal cortices. CONCLUSIONS: NMDAR synaptic deficits change causal interactions between neural signals at different levels of scale that correlate with schizophrenia-like deficits in cognitive control.

Dynamics of parietal neural activity during spatial cognitive processing.

Dynamic neural processing unrelated to changes in sensory input or motor output is likely to be a hallmark of cognitive operations. Here we show that neural representations of space in parietal cortex are dynamic while monkeys perform a spatial cognitive operation on a static visual stimulus. We recorded neural activity in area 7a during a visual maze task in which monkeys mentally followed a path without moving their eyes. We found that the direction of the followed path could be recovered from neuronal population activity. When the monkeys covertly processed a path that turned, the population representation of path direction shifted in the direction of the turn. This neural population dynamic took place during a period of unchanging visual input and showed characteristics of both serial and parallel processing. The data suggest that the dynamic evolution of parietal neuronal activity is associated with the progression of spatial cognitive operations.

Neural ensemble decoding reveals a correlate of viewer- to object-centered spatial transformation in monkey parietal cortex.

The parietal cortex contains representations of space in multiple coordinate systems including retina-, head-, body-, and world-based systems. Previously, we found that when monkeys are required to perform spatial computations on objects, many neurons in parietal area 7a represent position in an object-centered coordinate system as well. Because visual information enters the brain in a retina-centered reference frame, generation of an object-centered reference requires the brain to perform computation on the visual input. We provide evidence that area 7a contains a correlate of that computation. Specifically, area 7a contains neurons that code information in retina- and object-centered coordinate systems. The information in retina-centered coordinates emerges first, followed by the information in object-centered coordinates. We found that the strength and accuracy of these representations is correlated across trials. Finally, we found that retina-centered information could be used to predict subsequent object-centered signals, but not vice versa. These results are consistent with the hypothesis that either area 7a, or an area that precedes area 7a in the visual processing hierarchy, is performing the retina- to object-centered transformation.

Differential contribution of superior parietal and dorsal-lateral prefrontal cortices in copying.

In this study we examined the differential contribution of superior parietal cortex (SPC) and caudal dorsal-lateral prefrontal cortex (dlPFC) to drawing geometrical shapes. Monkeys were trained to draw triangles, squares, trapezoids and inverted triangles while we recorded the activity of small ensembles of neurons in caudal area 46 and areas 5 and 2 of parietal cortex. We analyzed the drawing factors encoded by individual neurons by fitting a step-wise general-linear model using as our dependent variable the firing rate averaged over segments of the produced trajectories. This analysis demonstrated that both cognitive (shape and segment serial position) and motor (maximum speed, position and direction of segment) factors modulated the activity of individual neurons. Furthermore, SPC had an enriched representation of both shape and motor factors, with the motor enrichment being stronger than the shape enrichment. Following this we used the activity in the simultaneously recorded neural ensembles to predict the hand velocity. In these analyses we found that the prediction of the hand velocity was better when we estimated different linear decoding functions for each shape than when we estimated a single function across shapes, although it was a subtle effect. Furthermore, we also found that ensembles of caudal dlPFC neurons carried considerable information about hand velocity, a purely motor factor. However, the SPC ensembles carried more information at the ensemble level as a function of the ensemble size than the caudal dlPFC ensembles, although the differences were not dramatic. Finally, an analysis of the response latencies of individual neurons showed that the caudal dlPFC representation was more sensory than the SPC representation, which was equally sensory and motor. Thus, this neurophysiological evidence suggests that both SPC and caudal dlPFC have a role in drawing, but that SPC plays a larger role in both the cognitive and the motor components.

Learning How Neurons Fail Inside of Networks: Nonhuman Primates Provide Critical Data for Psychiatry.

Advancing psychiatry requires understanding brain malfunction at a microscopic scale, where neurons and synapses operate under constraints imposed by behavior, cognition, and neural architecture. Nonhuman primates are unmatched in approximating the structural and computational environment of the human brain.

Representing spatial relationships in posterior parietal cortex: single neurons code object-referenced position.

The brain computes spatial relationships as necessary to achieve behavioral goals. Loss of this spatial cognitive ability after damage to posterior parietal cortex may contribute to constructional apraxia, a syndrome in which a patient's ability to reproduce spatial relationships between the parts of an object is disrupted. To explore neural correlates of object-relative spatial representation, we recorded neural activity in parietal area 7a of monkeys performing an object construction task. We found that neurons were activated as a function of the spatial relationship between a task-critical coordinate and a reference object. Individual neurons exhibited an object-relative spatial preference, such that different neural populations were activated when the spatial coordinate was located to the left or right of the reference object. In each case, the representation was robust to translation of the reference object, and neurons maintained their object-relative preference when the position of the object varied relative to the angle of gaze and viewer-centered frames of reference. This provides evidence that the activity of a subpopulation of parietal neurons active in the construction task represented relative position as referenced to an object and not absolute position with respect to the viewer.