Virginie Sterpenich

Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects

Using functional magnetic resonance imaging (fMRI), we recently demonstrated that nonmedicated patients with a first episode of unipolar major depression (MDD) compared to matched controls exhibited an abnormal neural filtering of irrelevant visual information (Desseilles et al., 2009). During scanning, subjects performed a visual attention task imposing two different levels of attentional load at fixation (low or high), while task-irrelevant colored stimuli were presented in the periphery. In the present study, we focused on the visuo-attentional system and used Dynamic Causal Modeling (DCM) on the same dataset to assess how attention influences a network of three dynamically-interconnected brain regions (visual areas V1 and V4, and intraparietal sulcus (P), differentially in MDD patients and healthy controls. Bayesian model selection (BMS) and model space partitioning (MSP) were used to determine the best model in each population. The best model for the controls revealed that the increase of parietal activity by high attention load was selectively associated with a negative modulation of P on V4, consistent with high attention reducing the processing of irrelevant colored peripheral stimuli. The best model accounting for the data from the MDD patients showed that both low and high attention levels exerted modulatory effects on P. The present results document abnormal effective connectivity across visuo-attentional networks in MDD, which likely contributes to deficient attentional filtering of information.

Visual avoidance in phobia: particularities in neural activity, autonomic responding, and cognitive risk evaluations

We investigated the neural mechanisms and the autonomic and cognitive responses associated with visual avoidance behavior in spider phobia. Spider phobic and control participants imagined visiting different forest locations with the possibility of encountering spiders, snakes, or birds (neutral reference category). In each experimental trial, participants saw a picture of a forest location followed by a picture of a spider, snake, or bird, and then rated their personal risk of encountering these animals in this context, as well as their fear. The greater the visual avoidance of spiders that a phobic participant demonstrated (as measured by eye tracking), the higher were her autonomic arousal and neural activity in the amygdala, orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and precuneus at picture onset. Visual avoidance of spiders in phobics also went hand in hand with subsequently reduced cognitive risk of encounters. Control participants, in contrast, displayed a positive relationship between gaze duration toward spiders, on the one hand, and autonomic responding, as well as OFC, ACC, and precuneus activity, on the other hand. In addition, they showed reduced encounter risk estimates when they looked longer at the animal pictures. Our data are consistent with the idea that one reason for phobics to avoid phobic information may be grounded in heightened activity in the fear circuit, which signals potential threat. Because of the absence of alternative efficient regulation strategies, visual avoidance may then function to down-regulate cognitive risk evaluations for threatening information about the phobic stimuli. Control participants, in contrast, may be characterized by a different coping style, whereby paying visual attention to potentially threatening information may help them to actively down-regulate cognitive evaluations of risk.

A nap to recap or how reward regulates hippocampal-prefrontal memory networks during daytime sleep in humans

Sleep plays a crucial role in the consolidation of newly acquired memories. Yet, how our brain selects the noteworthy information that will be consolidated during sleep remains largely unknown. Here we show that post-learning sleep favors the selectivity of long-term consolidation: when tested three months after initial encoding, the most important (i.e., rewarded, strongly encoded) memories are better retained, and also remembered with higher subjective confidence. Our brain imaging data reveals that the functional interplay between dopaminergic reward regions, the prefrontal cortex and the hippocampus contributes to the integration of rewarded associative memories. We further show that sleep spindles strengthen memory representations based on reward values, suggesting a privileged replay of information yielding positive outcomes. These findings demonstrate that post-learning sleep determines the neural fate of motivationally-relevant memories and promotes a value-based stratification of long-term memory stores. DOI: http://dx.doi.org/10.7554/eLife.07903.001

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Sleep sharpens sensory stimulus coding in human visual cortex after fear conditioning.

Efficient perceptual identification of emotionally-relevant stimuli requires optimized neural coding. Because sleep contributes to neural plasticity mechanisms, we asked whether the perceptual representation of emotionally-relevant stimuli within sensory cortices is modified after a period of sleep. We show combined effects of sleep and aversive conditioning on subsequent discrimination of face identity information, with parallel plasticity in the amygdala and visual cortex. After one night of sleep (but neither immediately nor after an equal waking interval), a fear-conditioned face was better detected when morphed with another identity. This behavioral change was accompanied by increased selectivity of the amygdala and face-responsive fusiform regions. Overnight neural changes can thus sharpen the representation of threat-related stimuli in cortical sensory areas, in order to improve detection in impoverished or ambiguous situations. These findings reveal an important role of sleep in shaping cortical selectivity to emotionally-relevant cues and thus promoting adaptive responses to new dangers.

Neural substrates of rumination tendency in non-depressed individuals

The tendency to ruminate, experienced by both healthy individuals and depressed patients, can be quantified by the Ruminative Response Scale (RRS). We hypothesized that brain activity associated with rumination tendency might not only occur at rest but also persist to some degree during a cognitive task. We correlated RRS with whole-brain fMRI data of 20 healthy subjects during rest and during a face categorization task with different levels of cognitive demands (easy or difficult conditions). Our results reveal that the more subjects tend to ruminate, the more they activate the left entorhinal region, both at rest and during the easy task condition, under low attentional demands. Conversely, lower tendency to ruminate correlates with greater activation of visual cortex during rest and activation of insula during the easy task condition. These results indicate a particular neural marker of the tendency to ruminate, corresponding to increased spontaneous activity in memory-related areas, presumably reflecting more internally driven trains of thoughts even during a concomitant task. Conversely, people who are not prone to ruminate show more externally driven activity.

Alterations in neural systems mediating cognitive flexibility and inhibition in mood disorders

Impairment in mental flexibility may be a key component contributing to cardinal cognitive symptoms among mood disorders patients, particularly thought control disorders. Impaired ability to switch from one thought to another might reflect difficulties in either generating new mental states, inhibiting previous states, or both. However, the neural underpinnings of impaired cognitive flexibility in mood disorders remain largely unresolved. We compared a group of mood disorders patients (n = 29) and a group of matched healthy subjects (n = 32) on a novel task‐switching paradigm involving happy and sad faces, that allowed us to separate generation of a new mental set (Switch Cost) and inhibition of the previous set during switching (Inhibition Cost), using fMRI. Behavioral data showed a larger Switch Cost in patients relative to controls, but the average Inhibition Cost did not differ between groups. At the neural level, a main effect of group was found with stronger activation of the subgenual cingulate cortex in patients. The larger Switch Cost in patients was reflected by a stronger recruitment of brain regions involved in attention and executive control, including the left intraparietal sulcus, precuneus, left inferior fontal gyrus, and right anterior cingulate. Critically, activity in the subgenual cingulate cortex was not downregulated by inhibition in patients relative to controls. In conclusion, mood disorder patients have exaggerated Switch Cost relative to controls, and this deficit in cognitive flexibility is associated with increased activation of the fronto‐parietal attention networks, combined with impaired modulation of the subgenual cingulate cortex when inhibition of previous mental states is needed. Hum Brain Mapp 37:1335‐1348, 2016.

A Role for the Locus Ceruleus in Reward Processing: Encoding Behavioral Energy Required for Goal-Directed Actions

The locus ceruleus (LC) is a small nucleus in the dorsal pons that innervates most of the forebrain, with the exception of the striatum. The LC is the main source of norepinephrine (NE) in the brain, and this LC–NE system is typically associated with arousal, vigilance states, attention, and

Ability to Maintain Internal Arousal and Motivation Modulates Brain Responses to Emotions

Persistence (PS) is defined as the ability to generate and maintain arousal and motivation internally in the absence of immediate external reward. Low PS individuals tend to become discouraged when expectations are not rapidly fulfilled. The goal of this study was to investigate whether individual differences in PS influence the recruitment of brain regions involved in emotional processing and regulation. In a functional MRI study, 35 subjects judged the emotional intensity of displayed pictures. When processing negative pictures, low PS (vs. high PS) subjects showed higher amygdala and right orbito-frontal cortex (OFC) activity but lower left OFC activity. This dissociation in OFC activity suggests greater prefrontal cortical asymmetry for approach/avoidance motivation, suggesting an avoidance response to aversive stimuli in low PS. For positive or neutral stimuli, low PS subjects showed lower activity in the amygdala, striatum, and hippocampus. These results suggest that low PS may involve an imbalance in processing distinct emotional inputs, with greater reactivity to aversive information in regions involved in avoidance behaviour (amygdala, OFC) and dampened response to positive and neutral stimuli across circuits subserving motivated behaviour (striatum, hippocampus, amygdala). Low PS affective style was associated with depression vulnerability. These findings in non-depressed subjects point to a neural mechanism whereby some individuals are more likely to show systematic negative emotional biases, as frequently observed in depression. The assessment of these individual differences, including those that may cause vulnerability to depressive disorders, would therefore constitute a promising approach to risk assessment for depression.

Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs

Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.