Amygdalar Excitation of Hippocampal Interneurons Can Lead to Emotion-driven Overgeneralization of Context.

Context is central to cognition: Detailed contextual representations enable flexible adjustment of behavior via comparison of the current situation with prior experience. Emotional experiences can greatly enhance contextual memory. However, sufficiently intense emotional signals can have the opposite effect, leading to weaker or less specific memories. How can emotional signals have such intensity-dependent effects? A plausible mechanistic account has emerged from recent anatomical data on the impact of the amygdala on the hippocampus in primates. In hippocampal CA3, the amygdala formed potent synapses on pyramidal neurons, calretinin (CR) interneurons, as well as parvalbumin (PV) interneurons. CR interneurons are known to disinhibit pyramidal neuron dendrites, whereas PV neurons provide strong perisomatic inhibition. This potentially counterintuitive connectivity, enabling amygdala to both enhance and inhibit CA3 activity, may provide a mechanism that can boost or suppress memory in an intensity-dependent way. To investigate this possibility, we simulated this connectivity pattern in a spiking network model. Our simulations revealed that moderate amygdala input can enrich CA3 representations of context through disinhibition via CR interneurons, but strong amygdalar input can impoverish CA3 activity through simultaneous excitation and feedforward inhibition via PV interneurons. Our model revealed an elegant circuit mechanism that mediates an affective "inverted U" phenomenonv: There is an optimal level of amygdalar input that enriches hippocampal context representations, but on either side of this zone, representations are impoverished. This circuit mechanism helps explain why excessive emotional arousal can disrupt contextual memory and lead to overgeneralization, as seen in severe anxiety and posttraumatic stress disorder.

Understanding the effects of serotonin in the brain through its role in the gastrointestinal tract.

The neuromodulatory arousal system imbues the nervous system with the flexibility and robustness required to facilitate adaptive behaviour. While there are well understood mechanisms linking dopamine, noradrenaline and acetylcholine to distinct behavioural states, similar conclusions have not been as readily available for serotonin. Fascinatingly, despite clear links between serotonergic function and cognitive capacities as diverse as reward processing, exploration, and the psychedelic experience, over 95% of the serotonin in the body is released in the gastrointestinal tract, where it controls digestive muscle contractions (peristalsis). Here, we argue that framing neural serotonin as a rostral extension of the gastrointestinal serotonergic system dissolves much of the mystery associated with the central serotonergic system. Specifically, we outline that central serotonin activity mimics the effects of a digestion/satiety circuit mediated by hypothalamic control over descending serotonergic nuclei in the brainstem. We review commonalities and differences between these two circuits, with a focus on the heterogeneous expression of different classes of serotonin receptors in the brain. Much in the way that serotonin-induced peristalsis facilitates the work of digestion, serotonergic influences over cognition can be reframed as performing the work of cognition. Extending this analogy, we argue that the central serotonergic system allows the brain to arbitrate between different cognitive modes as a function of serotonergic tone: low activity facilitates cognitive automaticity, whereas higher activity helps to identify flexible solutions to problems, particularly if and when the initial responses fail. This perspective sheds light on otherwise disparate capacities mediated by serotonin, and also helps to understand why there are such pervasive links between serotonergic pathology and the symptoms of psychiatric disorders.

Dead rats, dopamine, performance metrics, and peacock tails: proxy failure is an inherent risk in goal-oriented systems.

When a measure becomes a target, it ceases to be a good measure. For example, when standardized test scores in education become targets, teachers may start 'teaching to the test', leading to breakdown of the relationship between the measure--test performance--and the underlying goal--quality education. Similar phenomena have been named and described across a broad range of contexts, such as economics, academia, machine-learning, and ecology. Yet it remains unclear whether these phenomena bear only superficial similarities, or if they derive from some fundamental unifying mechanism. Here, we propose such a unifying mechanism, which we label proxy failure. We first review illustrative examples and their labels, such as the 'Cobra effect', 'Goodhart's law', and 'Campbell's law'. Second, we identify central prerequisites and constraints of proxy failure, noting that it is often only a partial failure or divergence. We argue that whenever incentivization or selection is based on an imperfect proxy measure of the underlying goal, a pressure arises which tends to make the proxy a worse approximation of the goal. Third, we develop this perspective for three concrete contexts, namely neuroscience, economics and ecology, highlighting similarities and differences. Fourth, we outline consequences of proxy failure, suggesting it is key to understanding the structure and evolution of goal-oriented systems. Our account draws on a broad range of disciplines, but we can only scratch the surface within each. We thus hope the present account elicits a collaborative enterprise, entailing both critical discussion as well as extensions in contexts we have missed.

Serial Prefrontal Pathways Are Positioned to Balance Cognition and Emotion in Primates.

The delicate balance among primate prefrontal networks is necessary for homeostasis and behavioral flexibility. Dorsolateral prefrontal cortex (dlPFC) is associated with cognition, while the most ventromedial subgenual cingulate area 25 (A25) is associated with emotion and emotional expression. Yet A25 is weakly connected with dlPFC, and it is unknown how the two regions communicate. In rhesus monkeys of both sexes, we investigated how these functionally distinct areas may interact through pregenual anterior cingulate area 32 (A32), which is strongly connected with both. We found that dlPFC innervated the deep layers of A32, while A32 innervated all layers of A25, mostly targeting spines of excitatory neurons. Approximately 20% of A32 terminations formed synapses on inhibitory neurons in A25, notably the powerful parvalbumin inhibitory neurons in the deep layers, and the disinhibitory calretinin neurons in the superficial layers. By innervating distinct inhibitory microenvironments in laminar compartments, A32 is positioned to tune activity in columns of A25. The circuitry of the sequential pathway indicates that when dlPFC is engaged, A32 can dampen A25 output through the parvalbumin inhibitory microsystem in the deep layers of A25. A32 thus may flexibly recruit or reduce activity in A25 to maintain emotional equilibrium, a process that is disrupted in depression. Moreover, pyramidal neurons in A25 had a heightened density of NMDARs, which are the targets of novel rapid-acting antidepressants. Pharmacologic antagonism of NMDARs in patients with depression may reduce excitability in A25, mimicking the effects of the neurotypical serial pathway identified here.SIGNIFICANCE STATEMENT The anterior cingulate is a critical hub in prefrontal networks through connections with functionally distinct areas. Dorsolateral and polar prefrontal areas that are associated with complex cognition are connected with the anterior cingulate in a pattern that allows them to indirectly control downstream activity from the anterior cingulate to the subgenual cingulate, which is associated with heightened activity and negative affect in depression. This set of pathways provides a circuit mechanism for emotional regulation, with the anterior cingulate playing a balancing role for integration of cognitive and emotional processes. Disruption of these pathways may perturb network function and the ability to regulate cognitive and affective processes based on context.

Opposite development of short- and long-range anterior cingulate pathways in autism.

Autism has been linked with the changes in brain connectivity that disrupt neural communication, especially involving frontal networks. Pathological changes in white matter are evident in adults with autism, particularly affecting axons below the anterior cingulate cortices (ACC). It is still unknown whether axon pathology appears early or late in development and whether it changes or not from childhood through adulthood. To address these questions, we examined typical and pathological development of about 1 million axons in post-mortem brains of children, adolescents, and adults with and without autism (ages 3-67 years). We used high-resolution microscopy to systematically sample and study quantitatively the fine structure of myelinated axons in the white matter below ACC. We provide novel evidence of changes in the density, size and trajectories of ACC axons in typical postnatal development from childhood through adulthood. Against the normal profile of axon development, our data revealed lower density of myelinated axons that connect ACC with neighboring cortices in children with autism. In the course of development the proportion of thin axons, which form short-range pathways, increased significantly in individuals with autism, but remained flat in controls. In contrast, the relative proportion of thick axons, which form long-range pathways, increased from childhood to adulthood in the control group, but decreased in autism. Our findings provide a timeline for profound changes in axon density and thickness below ACC that affect axon physiology in a direction suggesting bias in short over distant neural communication in autism. Importantly, measures of axon density, myelination, and orientation provide white matter anisotropy/diffusivity estimates at the level of single axons. The structural template established can be used to compare with measures obtained from imaging in living subjects, and guide analysis of functional and structural imaging data from humans for comparison with pathological states.

Mirror trends of plasticity and stability indicators in primate prefrontal cortex.

Research on plasticity markers in the cerebral cortex has largely focused on their timing of expression and role in shaping circuits during critical and normal periods. By contrast, little attention has been focused on the spatial dimension of plasticity-stability across cortical areas. The rationale for this analysis is based on the systematic variation in cortical structure that parallels functional specialization and raises the possibility of varying levels of plasticity. Here, we investigated in adult rhesus monkeys the expression of markers related to synaptic plasticity or stability in prefrontal limbic and eulaminate areas that vary in laminar structure. Our findings revealed that limbic areas are impoverished in three markers of stability: intracortical myelin, the lectin Wisteria floribunda agglutinin, which labels perineuronal nets, and parvalbumin, which is expressed in a class of strong inhibitory neurons. By contrast, prefrontal limbic areas were enriched in the enzyme calcium/calmodulin-dependent protein kinase II (CaMKII), known to enhance plasticity. Eulaminate areas have more elaborate laminar architecture than limbic areas and showed the opposite trend: they were enriched in markers of stability and had lower expression of the plasticity-related marker CaMKII. The expression of glial fibrillary acidic protein (GFAP), a marker of activated astrocytes, was also higher in limbic areas, suggesting that cellular stress correlates with the rate of circuit reshaping. Elevated markers of plasticity may endow limbic areas with flexibility necessary for learning and memory within an affective context, but may also render them vulnerable to abnormal structural changes, as seen in neurologic and psychiatric diseases.

The Emotional Gatekeeper: A Computational Model of Attentional Selection and Suppression through the Pathway from the Amygdala to the Inhibitory Thalamic Reticular Nucleus.

In a complex environment that contains both opportunities and threats, it is important for an organism to flexibly direct attention based on current events and prior plans. The amygdala, the hub of the brain's emotional system, is involved in forming and signaling affective associations between stimuli and their consequences. The inhibitory thalamic reticular nucleus (TRN) is a hub of the attentional system that gates thalamo-cortical signaling. In the primate brain, a recently discovered pathway from the amygdala sends robust projections to TRN. Here we used computational modeling to demonstrate how the amygdala-TRN pathway, embedded in a wider neural circuit, can mediate selective attention guided by emotions. Our Emotional Gatekeeper model demonstrates how this circuit enables focused top-down, and flexible bottom-up, allocation of attention. The model suggests that the amygdala-TRN projection can serve as a unique mechanism for emotion-guided selection of signals sent to cortex for further processing. This inhibitory selection mechanism can mediate a powerful affective 'framing' effect that may lead to biased decision-making in highly charged emotional situations. The model also supports the idea that the amygdala can serve as a relevance detection system. Further, the model demonstrates how abnormal top-down drive and dysregulated local inhibition in the amygdala and in the cortex can contribute to the attentional symptoms that accompany several neuropsychiatric disorders.

The inevitable inequality of cortical columns.

The idea of columns as an organizing cortical unit emerged from physiologic studies in the sensory systems. Connectional studies and molecular markers pointed to widespread presence of modular label that necessitated revision of the classical concept of columns. The general principle of cortical systematic variation in laminar structure is at the core of cortical organization. Systematic variation can be traced to the phylogenetically ancient limbic cortices, which have the simplest laminar structure, and continues through eulaminate cortices that show sequential elaboration of their six layers. Connections are governed by relational rules, whereby columns or modules with a vertical organization represent the feedforward mode of communication from earlier- to later processing cortices. Conversely, feedback connections are laminar-based and connect later- with earlier processing areas; both patterns are established in development. Based on studies in primates, the columnar/modular pattern of communication appears to be newer in evolution, while the broadly based laminar pattern represents an older system. The graded variation of cortices entails a rich variety of patterns of connections into modules, layers, and mixed arrangements as the laminar and modular patterns of communication intersect in the cortex. This framework suggests an ordered architecture poised to facilitate seamless recruitment of areas in behavior, in patterns that are affected in diseases of developmental origin.

The cortical spectrum: A robust structural continuum in primate cerebral cortex revealed by histological staining and magnetic resonance imaging.

High-level characterizations of the primate cerebral cortex sit between two extremes: on one end the cortical mantle is seen as a mosaic of structurally and functionally unique areas, and on the other it is seen as a uniform six-layered structure in which functional differences are defined solely by extrinsic connections. Neither of these extremes captures the crucial neuroanatomical finding: that the cortex exhibits systematic gradations in architectonic structure. These gradations have been shown to predict cortico-cortical connectivity, which in turn suggests powerful ways to ground connectomics in anatomical structure, and by extension cortical function. A challenge to widespread use of this concept is the labor-intensive and invasive nature of histological staining, which is the primary means of recognizing anatomical gradations. Here we show that a novel computational analysis technique can provide a coarse-grained picture of cortical variation. For each of 78 cortical areas spanning the entire cortical mantle of the rhesus macaque, we created a high dimensional set of anatomical features derived from captured images of cortical tissue stained for myelin and SMI-32. The method involved semi-automated de-noising of images, and enabled comparison of brain areas without hand-labeling of features such as layer boundaries. We applied multidimensional scaling (MDS) to the dataset to visualize similarity among cortical areas. This analysis shows a systematic variation between weakly laminated (limbic) cortices and sharply laminated (eulaminate) cortices. We call this smooth continuum the "cortical spectrum". We also show that this spectrum is visible within subsystems of the cortex: the occipital, parietal, temporal, motor, prefrontal, and insular cortices. We compared the MDS-derived spectrum with a spectrum produced using T1- and T2-weighted magnetic resonance imaging (MRI) data derived from macaque, and found close agreement of the two coarse-graining methods. This suggests that T1w/T2w data, routinely obtained in human MRI studies, can serve as an effective proxy for data derived from high-resolution histological methods. More generally, this approach shows that the cortical spectrum is robust to the specific method used to compare cortical areas, and is therefore a powerful tool to understand the principles of organization of the primate cortex.

It's about time: Linking dynamical systems with human neuroimaging to understand the brain.

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

Visual Attention Deficits in Schizophrenia Can Arise From Inhibitory Dysfunction in Thalamus or Cortex.

Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.

Distinction of Neurons, Glia and Endothelial Cells in the Cerebral Cortex: An Algorithm Based on Cytological Features.

The estimation of the number or density of neurons and types of glial cells and their relative proportions in different brain areas are at the core of rigorous quantitative neuroanatomical studies. Unfortunately, the lack of detailed, updated, systematic and well-illustrated descriptions of the cytology of neurons and glial cell types, especially in the primate brain, makes such studies especially demanding, often limiting their scope and broad use. Here, following an extensive analysis of histological materials and the review of current and classical literature, we compile a list of precise morphological criteria that can facilitate and standardize identification of cells in stained sections examined under the microscope. We describe systematically and in detail the cytological features of neurons and glial cell types in the cerebral cortex of the macaque monkey and the human using semithin and thick sections stained for Nissl. We used this classical staining technique because it labels all cells in the brain in distinct ways. In addition, we corroborate key distinguishing characteristics of different cell types in sections immunolabeled for specific markers counterstained for Nissl and in ultrathin sections processed for electron microscopy. Finally, we summarize the core features that distinguish each cell type in easy-to-use tables and sketches, and structure these key features in an algorithm that can be used to systematically distinguish cellular types in the cerebral cortex. Moreover, we report high inter-observer algorithm reliability, which is a crucial test for obtaining consistent and reproducible cell counts in unbiased stereological studies. This protocol establishes a consistent framework that can be used to reliably identify and quantify cells in the cerebral cortex of primates as well as other mammalian species in health and disease.

The intercalated nuclear complex of the primate amygdala.

The organization of the inhibitory intercalated cell masses (IM) of the primate amygdala is largely unknown despite their key role in emotional processes. We studied the structural, topographic, neurochemical and intrinsic connectional features of IM neurons in the rhesus monkey brain. We found that the intercalated neurons are not confined to discrete cell clusters, but form a neuronal net that is interposed between the basal nuclei and extends to the dorsally located anterior, central, and medial nuclei of the amygdala. Unlike the IM in rodents, which are prominent in the anterior half of the amygdala, the primate inhibitory net stretched throughout the antero-posterior axis of the amygdala, and was most prominent in the central and posterior extent of the amygdala. There were two morphologic types of intercalated neurons: spiny and aspiny. Spiny neurons were the most abundant; their somata were small or medium size, round or elongated, and their dendritic trees were round or bipolar, depending on location. The aspiny neurons were on average slightly larger and had varicose dendrites with no spines. There were three non-overlapping neurochemical populations of IM neurons, in descending order of abundance: (1) Spiny neurons that were positive for the striatal associated dopamine- and cAMP-regulated phosphoprotein (DARPP-32+); (2) Aspiny neurons that expressed the calcium-binding protein calbindin (CB+); and (3) Aspiny neurons that expressed nitric oxide synthase (NOS+). The unique combinations of structural and neurochemical features of the three classes of IM neurons suggest different physiological properties and function. The three types of IM neurons were intermingled and likely interconnected in distinct ways, and were innervated by intrinsic neurons within the amygdala, or by external sources, in pathways that underlie fear conditioning and anxiety.

Anatomy and computational modeling of networks underlying cognitive-emotional interaction.

The classical dichotomy between cognition and emotion equated the first with rationality or logic and the second with irrational behaviors. The idea that cognition and emotion are separable, antagonistic forces competing for dominance of mind has been hard to displace despite abundant evidence to the contrary. For instance, it is now known that a pathological absence of emotion leads to profound impairment of decision making. Behavioral observations of this kind are corroborated at the mechanistic level: neuroanatomical studies reveal that brain areas typically described as underlying either cognitive or emotional processes are linked in ways that imply complex interactions that do not resemble a simple mutual antagonism. Instead, physiological studies and network simulations suggest that top-down signals from prefrontal cortex realize "cognitive control" in part by either suppressing or promoting emotional responses controlled by the amygdala, in a way that facilitates adaptation to changing task demands. Behavioral, anatomical, and physiological data suggest that emotion and cognition are equal partners in enabling a continuum or matrix of flexible behaviors that are subserved by multiple brain regions acting in concert. Here we focus on neuroanatomical data that highlight circuitry that structures cognitive-emotional interactions by directly or indirectly linking prefrontal areas with the amygdala. We also present an initial computational circuit model, based on anatomical, physiological, and behavioral data to explicitly frame the learning and performance mechanisms by which cognition and emotion interact to achieve flexible behavior.

Computational perspectives on forebrain microcircuits implicated in reinforcement learning, action selection, and cognitive control.

Abundant new information about signaling pathways in forebrain microcircuits presents many challenges, and opportunities for discovery, to computational neuroscientists who strive to bridge from microcircuits to flexible cognition and action. Accurate treatment of microcircuit pathways is especially critical for creating models that correctly predict the outcomes of candidate neurological therapies. Recent models are trying to specify how cortical circuits that enable planning and voluntary actions interact with adaptive subcortical microcircuits in the basal ganglia. The basal ganglia are strongly implicated in reinforcement learning, and in all behavior and cognition over which the frontal lobes exert flexible control. The persisting role of the basal ganglia shows that ancient vertebrate designs for motivated action selection proved adaptable enough to support many "modern" behavioral innovations, including fluent generation of language and speech. This paper summarizes how recent models have incorporated realistic representations of microcircuit features, and have begun to trace their computational implications. Also summarized are recent empirical discoveries that provide guidance regarding how to formulate the rules for synaptic modification that govern learning in cortico-striatal pathways. Such efforts are contributing to an emerging synthesis based on an interlocking set of computational hypotheses regarding cortical interactions with basal ganglia and thalamic nuclei. These hypotheses specify how specialized microcircuits solve learning and control problems inherent to the brain's parallel design.