Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

Neurons in auditory cortex show highly diverse responses to sounds. This study suggests that neuronal type inferred from baseline firing properties accounts for much of this diversity, with a subpopulation of bursting units being specialized for precise temporal encoding.

The Abnormal Functional Connectivity in the Locus Coeruleus-Norepinephrine System Associated With Anxiety Symptom in Chronic Insomnia Disorder

BACKGROUND

Mental syndromes such as anxiety and depression are common comorbidities in patients with chronic insomnia disorder (CID). The locus coeruleus noradrenergic (LC-NE) system is considered to be crucial for modulation of emotion and sleep/wake cycle. LC-NE system is also a critical mediator of the stress-induced anxiety. However, whether the LC-NE system contributes to the underlying mechanism linking insomnia and these comorbidities remain unclear. This study aimed to investigate the LC-NE system alterations in patients with insomnia and its relationship with depression and anxiety symptoms.

MATERIALS AND METHODS

Seventy patients with CID and 63 matched good sleep control (GSC) subjects were recruited and underwent resting-state functional MRI scan. LC-NE functional network was constructed by using seed-based functional connectivity (FC) analysis. The alterations in LC-NE FC network in patients with CID and their clinical significance was explored.

RESULTS

Compared with GSC group, the CID group showed decreased left LC-NE FC in the left inferior frontal gyrus, while they had increased LC-NE FC in the left supramarginal gyrus and the left middle occipital gyrus (MOG). For the right LC-NE FC network, decreased FC was found in left dorsal anterior cingulate cortex (dACC). Interesting, the increased LC-NE FC was located in sensory cortex, while decreased LC-NE FC was located in frontal control cortex. In addition, the FC between the left LC and left MOG was associated with the duration of the disease, while abnormal FC between right LC and left dACC was associated with the anxiety scores in patients with CID.

CONCLUSION

The present study found abnormal LC-NE functional network in patients with CID, and the altered LC-NE function in dACC was associated with anxiety symptoms in CID. The present study substantially extended our understanding of the neuropathological basis of CID and provided the potential treatment target for CID patients who also had anxiety.

Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques

One of the most influential accounts of central orbitofrontal cortex-that it mediates behavioral flexibility-has been challenged by the finding that discrimination reversal in macaques, the classic test of behavioral flexibility, is unaffected when lesions are made by excitotoxin injection rather than aspiration. This suggests that the critical brain circuit mediating behavioral flexibility in reversal tasks lies beyond the central orbitofrontal cortex. To determine its identity, a group of nine macaques were taught discrimination reversal learning tasks, and its impact on gray matter was measured. Magnetic resonance imaging scans were taken before and after learning and compared with scans from two control groups, each comprising 10 animals. One control group learned discrimination tasks that were similar but lacked any reversal component, and the other control group engaged in no learning. Gray matter changes were prominent in posterior orbitofrontal cortex/anterior insula but were also found in three other frontal cortical regions: lateral orbitofrontal cortex (orbital part of area 12 [12o]), cingulate cortex, and lateral prefrontal cortex. In a second analysis, neural activity in posterior orbitofrontal cortex/anterior insula was measured at rest, and its pattern of coupling with the other frontal cortical regions was assessed. Activity coupling increased significantly in the reversal learning group in comparison with controls. In a final set of experiments, we used similar structural imaging procedures and analyses to demonstrate that aspiration lesion of central orbitofrontal cortex, of the type known to affect discrimination learning, affected structure and activity in the same frontal cortical circuit. The results identify a distributed frontal cortical circuit associated with behavioral flexibility.

A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover's Distance

Neural spike train analysis methods are mainly used for understanding the temporal aspects of neural information processing. One approach is to measure the dissimilarity between the spike trains of a pair of neurons, often referred to as the spike train distance. The spike train distance has been often used to classify neuronal units with similar temporal patterns. Several methods to compute spike train distance have been developed so far. Intuitively, a desirable distance should be the shortest length between two objects. The Earth Mover’s Distance (EMD) can compute spike train distance by measuring the shortest length between two spike trains via shifting a fraction of spikes from one spike train to another. The EMD could accurately measure spike timing differences, temporal similarity, and spikes time synchrony. It is also robust to firing rate changes. Victor and Purpura (1996) distance measures the minimum cost between two spike trains. Although it also measures the shortest path between spike trains, its output can vary with the time-scale parameter. In contrast, the EMD measures distance in a unique way by calculating the genuine shortest length between spike trains. The EMD also outperforms other existing spike train distance methods in measuring various aspects of the temporal characteristics of spike trains and in robustness to firing rate changes. The EMD can effectively measure the shortest length between spike trains without being considerably affected by the overall firing rate difference between them. Hence, it is suitable for pure temporal coding exclusively, which is a predominant premise underlying the present study.

Cortical thickness contributes to cognitive heterogeneity in patients with type 2 diabetes mellitus

The aim of this study was to investigate cerebral cortical thickness alterations in patients with type 2 diabetes mellitus (T2DM) and their association with mild cognitive impairment (MCI).Thirty T2DM patients without MCI, 30 T2DM patients with MCI, and 30 healthy controls were recruited. All subjects underwent high-resolution sagittal T1-weighted structural imaging using a 3-dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence. The cortical thicknesses of the whole brain of the 3 groups were analyzed and compared using analysis of variance (ANOVA) test. Partial correlations between the cortical thicknesses of each brain region and standard laboratory testing data were analyzed for the T2DM without MCI group. The associations between cortical thicknesses and neuropsychological scale scores were also analyzed in the T2DM with MCI group.Compared with the healthy controls, the T2DM without MCI group showed statistically significant reduction in the cortical thickness of the left posterior cingulate gyrus, right isthmus cingulate gyrus, middle temporal gyrus, paracentral lobule, and transverse temporal gyrus. No significant correlation was found between the standard laboratory testing data and the cortical thicknesses of these cerebral regions. Compared with the T2DM without MCI group, the cortical thickness alterations in the T2DM with MCI group were bidirectional. Increased cortical thickness was found in the left parahippocampal gyrus and the right isthmus cingulate gyrus. Decreased cortical thickness was observed in the left pars triangularis and the right pars opercularis. Significant correlations were found between the cortical thickness of the right pars opercularis and the Complex Figure Test-delayed recall scores (r = 0.464, ρ = 0.015), Trail Making Test A consuming time (r = -0.454, ρ = 0.017), and Montreal Cognitive Assessment scores (r = 0.51, ρ = 0.007).T2DM could influence the gray matter of several brain regions. The cortical thickness reduction of the right pars opercularis may be a biomarker of cognitive impairment and play an important role in its pathophysiological mechanism.

Value, search, persistence and model updating in anterior cingulate cortex

Dorsal anterior cingulate cortex (dACC) carries a wealth of value-related information necessary for regulating behavioral flexibility and persistence. It signals error and reward events informing decisions about switching or staying with current behavior. During decision-making, it encodes the average value of exploring alternative choices (search value), even after controlling for response selection difficulty, and during learning, it encodes the degree to which internal models of the environment and current task must be updated. dACC value signals are derived in part from the history of recent reward integrated simultaneously over multiple time scales, thereby enabling comparison of experience over the recent and extended past. Such ACC signals may instigate attentionally demanding and difficult processes such as behavioral change via interactions with prefrontal cortex. However, the signal in dACC that instigates behavioral change need not itself be a conflict or difficulty signal.

Cell assemblies at multiple time scales with arbitrary lag constellations

Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

DOI:http://dx.doi.org/10.7554/eLife.19428.001

Prefrontal Markers and Cognitive Performance Are Dissociated during Progressive Dopamine Lesion

Dopamine is thought to directly influence the neurophysiological mechanisms of both performance monitoring and cognitive control-two processes that are critically linked in the production of adapted behaviour. Changing dopamine levels are also thought to induce cognitive changes in several neurological and psychiatric conditions. But the working model of this system as a whole remains untested. Specifically, although many researchers assume that changing dopamine levels modify neurophysiological mechanisms and their markers in frontal cortex, and that this in turn leads to cognitive changes, this causal chain needs to be verified. Using longitudinal recordings of frontal neurophysiological markers over many months during progressive dopaminergic lesion in non-human primates, we provide data that fail to support a simple interaction between dopamine, frontal function, and cognition. Feedback potentials, which are performance-monitoring signals sometimes thought to drive successful control, ceased to differentiate feedback valence at the end of the lesion, just before clinical motor threshold. In contrast, cognitive control performance and beta oscillatory markers of cognitive control were unimpaired by the lesion. The differing dynamics of these measures throughout a dopamine lesion suggests they are not all driven by dopamine in the same way. These dynamics also demonstrate that a complex non-linear set of mechanisms is engaged in the brain in response to a progressive dopamine lesion. These results question the direct causal chain from dopamine to frontal physiology and on to cognition. They imply that biomarkers of cognitive functions are not directly predictive of dopamine loss.

Long-Range Attention Networks: Circuit Motifs Underlying Endogenously Controlled Stimulus Selection

Attention networks comprise brain areas whose coordinated activity implements stimulus selection. This selection is reflected in spatially referenced priority maps across frontal-parietal-collicular areas and is controlled through interactions with circuits representing behavioral goals, including prefrontal, cingulate, and striatal circuits, among others. We review how these goal-providing structures control stimulus selection through long-range dynamic projection motifs. These motifs (i) combine feature-tuned subnetworks to a distributed priority map, (ii) establish endogenously controlled, long-range coherent activity at 4-10 Hz theta and 12-30 Hz beta-band frequencies, and (iii) are composed of unique cell types implementing long-range networks through disynaptic disinhibition, dendritic gating, and feedforward inhibitory gain control. This evidence reveals common circuit motifs used to coordinate attentionally selected information across multi-node brain networks during goal-directed behavior.

Multiple signals in anterior cingulate cortex

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There are multiple signals in anterior cingulate cortex (ACC).

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ACC activity reflects value of behavioural change even after controlling for difficulty.

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ACC activity reflects updating of internal models even after controlling for difficulty.

Activity in anterior cingulate cortex (ACC) has been linked both to commitment to a course of action, even when it is associated with costs, and to exploring or searching for alternative courses of action. Here we review evidence that this is due to the presence of multiple signals in ACC reflecting the updating of beliefs and internal models of the environment and encoding aspects of choice value, including the average value of choices afforded by the environment (‘search value’). We contrast this evidence with the influential view that ACC activity is better described as reflecting task difficulty. A consideration of cortical neural network properties explains why ACC may carry such signals and also exhibit sensitivity to task difficulty.