Periaqueductal gray matter and medial prefrontal cortex reflect negative prediction errors during differential conditioning.

Computational models of associative learning posit that negative prediction errors (PEs) arising from the omission of aversive outcomes weaken aversive Pavlovian associations during differential conditioning and extinction. It is possible that negative PEs may underlie exaggerated conditioned responses to the conditioned stimulus not paired with an aversitve outcome (CS-) during differential conditioning and to the conditioned stimulus originally paired with a aversive outcome (CS+) during extinction in patients with clinical anxiety disorders. Although previous research has demonstrated that manipulations of the periaqueductal gray matter (PAG) interfere with extinction learning in animals, the role of the PAG in processing negative PEs within the human brain is presently unclear. We set out to investigate how PAG responses and connectivity are impacted by negative PEs using ultra-high-field (7 T) functional magnetic resonance imaging and hierarchical Bayesian analysis. During differential conditioning, negative PEs were associated with larger responses within the lateral and dorsolateral PAG and increased connectivity between the dorsolateral PAG and medial areas of Brodmann area 9. Collectively, these results shed light on the association between activity within the PAG and medial prefrontal cortex and the omission of aversive outcomes during Pavlovian learning.

The Posterior Cingulate Cortex Reflects the Impact of Anxiety on Drift Rates During Cognitive Processing.

BACKGROUND: Generally, anxiety is thought to impair ongoing cognitive operations. Surprisingly, however, anxiety has been shown to improve performance during the Go/NoGo task. Understanding how anxiety can facilitate task performance may shed light on avenues to address the cognitive deficits commonly associated with anxiety. METHODS: A total of 39 participants (mean age ± SD = 27.5 ± 7.22 years; 18 women) performed a Go/NoGo task during periods of safety and periods of experimental anxiety, induced using the unpredictable delivery of aversive stimuli. Computational analysis and ultrahigh field (7T) functional magnetic resonance imaging were used to determine how induced anxiety affected computational processes and blood oxygen level-dependent responses during the task. RESULTS: Induced anxiety improved accuracy during the Go/NoGo task. Induced anxiety was associated with an amplified drift rate process, which is thought to reflect increased informational uptake. In addition, changes in drift rate during the anxiety condition were associated with enhanced blood oxygen level-dependent responses within the posterior cingulate cortex during Go trials. CONCLUSIONS: These results may reflect the impact of induced anxiety on the activity of neurons within the posterior cingulate cortex, whose activity patterns mimic the buildup of evidence accumulation. Collectively, these results shed light on the mechanisms underlying facilitated task performance and suggest that anxiety can improve cognitive processing by enhancing information uptake and increasing activity within the posterior cingulate cortex.

Dynamic Time Warping Identifies Functionally Distinct fMRI Resting State Cortical Networks Specific to VTA and SNc: A Proof of Concept.

Functional connectivity (FC) is determined by similarity between functional magnetic resonance imaging (fMRI) signals from distinct brain regions. However, traditional FC analyses ignore temporal phase differences. Here, we addressed this limitation, using dynamic time warping (DTW) within a machine-learning framework, to study cortical FC patterns of 2 spatially adjacent but functionally distinct subcortical regions, namely Substantia Nigra Pars Compacta (SNc) and ventral tegmental area (VTA). We evaluate: 1) the influence of pair of brain regions considered, 2) the influence of warping window sizes, 3) the classification efficacy of DTW, and 4) the uniqueness of features identified. Whole brain 7 Tesla resting state fMRI scans from 81 healthy participants were used. FC between 2 subcortical regions of interests (ROIs) and 360 cortical parcels were computed using: 1) Pearson correlations (PCs), 2) dynamic time-warped PCs (DTW-PC). The separability of SNc-cortical and VTA-cortical network was validated on 40 participants and tested on the remaining 41, using a support vector machine (SVM). The SVM separated the SNc-cortical versus VTA-cortical network with 74.39 and 97.56% test accuracy using PC and DTW-PC, respectively. SVM-recursive feature elimination yielded 20 DTW-PC features that most strongly contributed to the separation of the networks and revealed novel VTA versus SNc preferential connections (P < 0.05, Bonferroni-Holm corrected).

Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior.

Uncovering brain-behavior mechanisms is the ultimate goal of neuroscience. A formidable amount of discoveries has been made in the past 50 years, but the very essence of brain-behavior mechanisms still escapes us. The recent exploitation of machine learning (ML) tools in neuroscience opens new avenues for illuminating these mechanisms. A key advantage of ML is to enable the treatment of large data, combing highly complex processes. This essay provides a glimpse of how ML tools could test a heuristic neural systems model of motivated behavior, the triadic neural systems model, which was designed to understand behavioral transitions in adolescence. This essay previews analytic strategies, using fictitious examples, to demonstrate the potential power of ML to decrypt the neural networks of motivated behavior, generically and across development. Of note, our intent is not to provide a tutorial for these analyses nor a pipeline. The ultimate objective is to relate, as simply as possible, how complex neuroscience constructs can benefit from ML methods for validation and further discovery. By extension, the present work provides a guide that can serve to query the mechanisms underlying the contributions of prefrontal circuits to emotion regulation. The target audience concerns mainly clinical neuroscientists. As a caveat, this broad approach leaves gaps, for which references to comprehensive publications are provided.

The influence of astrocytes on the width of orientation hypercolumns in visual cortex: A computational perspective.

Orientation preference maps (OPMs) are present in carnivores (such as cats and ferrets) and primates but are absent in rodents. In this study we investigate the possible link between astrocyte arbors and presence of OPMs. We simulate the development of orientation maps with varying hypercolumn widths using a variant of the Laterally Interconnected Synergetically Self-Organizing Map (LISSOM) model, the Gain Control Adaptive Laterally connected (GCAL) model, with an additional layer simulating astrocytic activation. The synaptic activity of V1 neurons is given as input to the astrocyte layer. The activity of this astrocyte layer is now used to modulate bidirectional plasticity of lateral excitatory connections in the V1 layer. By simply varying the radius of the astrocytes, the extent of lateral excitatory neuronal connections can be manipulated. An increase in the radius of lateral excitatory connections subsequently increases the size of a single hypercolumn in the OPM. When these lateral excitatory connections become small enough the OPM disappears and a salt-and-pepper organization emerges.

Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model.

Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as "hemoneural hypothesis" postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of "vascular units" supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its "projective field." Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder neural network.

A Global Orientation Map in the Primary Visual Cortex (V1): Could a Self Organizing Model Reveal Its Hidden Bias?

A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial stimuli. The radial bias describes the preference of the visual system toward orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation (rc ). The rc between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. The results also suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps.

The mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex: an activity-dependent developmental model.

Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Theoretically such a map has been approximated by a complex log map. Neural models with correlational learning have explained the development of other visual maps like orientation maps and ocular-dominance maps. In this paper it is demonstrated that activity based mechanisms can drive a self-organizing map (SOM) into such a configuration that dilations and rotations of a particular image (in this case a rectangular bar) are mapped onto orthogonal axes. We further demonstrate using the Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model, with an appropriate boundary and realistic initial conditions, that a retinotopic map which maps eccentricity and meridional angle to the horizontal and vertical axes respectively can be developed. This developed map bears a strong resemblance to the complex log map. We also simulated lesion studies which indicate that the lateral excitatory connections play a crucial role in development of the retinotopic map.