The nucleus locus coeruleus modulatory effect on memory formation: A literature review

The nucleus locus coeruleus (LC), the main source of norepinephrine in the brain, is connected to memory processing regions such as the hippocampus and baso-lateral amygdala (BLA). The LC and its mostly associated noradrenergic projections, play an important role in memory formation parallel to other neurotransmitter systems. It has been suggested that the unique response characteristics of LC to various situations strengthens different memories formation. Here, we review key related findings of LC effect on memory (avoidance, spatial, cognitive) formation, memory processing regions, memory molecular mechanisms as well as its role in memory related disorders. Literature review was conducted by extensive search on ISI, PubMed and Scopus, online databases from May 2021 to July 2021. According to the obtained results, LC noradrenergic projections to memory processing areas of the brain, can modulate the encoding, consolidation, and retrieval for different memory types. Also, the LC regulates neurogenesis and neural plasticity in different areas of the brain. Evidences suggested that dysfunction of the LC and its associated noradrenergic system may lead to cognitive impairment or a variety of memory-related disorders, including Alzheimer's disease. Finally, it can be concluded that the locus coeruleus noradrenergic system may be a suitable target for the treatment of different memory/cognitive disorders

Distinct interactions between fronto-parietal and default mode networks in impaired consciousness

Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state

Autonomic responses to emotional linguistic stimuli and amplitude of low-frequency fluctuations predict outcome after severe brain injury

An accurate prognosis on the outcome of brain-injured patients with disorders of consciousness (DOC) remains a significant challenge, especially in the acute stage. In this study, we applied a multiple-technique approach to provide accurate predictions on functional outcome after 6 months in 15 acute DOC patients. Electrophysiological correlates of implicit cognitive processing of verbal stimuli and data-driven voxel-wise resting-state fMRI signals, such as the fractional amplitude of low-frequency fluctuations (fALFF), were employed. Event-related electrodermal activity, an index of autonomic activation, was recorded in response to emotional words and pseudo-words at baseline (T0). On the same day, patients also underwent a resting-state fMRI scan. Six months later (T1), patients were classified as outcome-negative and outcome-positive using a standard functional outcome scale. We then revisited the baseline measures to test their predictive power for the functional outcome measured at T1. We found that only outcome-positive patients had an earlier, higher autonomic response for words compared to pseudo-words, a pattern similar to that of healthy awake controls. Furthermore, DOC patients showed reduced fALFF in the posterior cingulate cortex (PCC), a brain region that contributes to autonomic regulation and awareness. The event-related electrodermal marker of residual cognitive functioning was found to have a significant correlation with residual local neuronal activity in the PCC. We propose that a residual autonomic response to cognitively salient stimuli, together with a preserved resting-state activity in the PCC, can provide a useful prognostic index in acute DOC

Claustrum, consciousness, and time perception

The claustrum has been proposed as a possible neural candidate for the coordination of conscious experience due to its extensive ‘connectome’. Herein we propose that the claustrum contributes to consciousness by supporting the temporal integration of cortical oscillations in response to multisensory input. A close link between conscious awareness and interval timing is suggested by models of consciousness and conjunctive changes in meta-awareness and timing in multiple contexts and conditions. Using the striatal beat-frequency model of interval timing as a framework, we propose that the claustrum integrates varying frequencies of neural oscillations in different sensory cortices into a coherent pattern that binds different and overlapping temporal percepts into a unitary conscious representation. The proposed coordination of the striatum and claustrum allows for time-based dimensions of multisensory integration and decision-making to be incorporated into consciousness

Global structural integrity and effective connectivity in patients with disorders of consciousness

Background: Previous studies have separately reported impaired functional, structural, and effective connectivity in patients with disorders of consciousness (DOC). The perturbational complexity index (PCI) is a transcranial magnetic stimulation (TMS) derived marker of effective connectivity. The global fractional anisotropy (FA) is a marker of structural integrity. Little is known about how these parameters are related to each other. Objective: We aimed at testing the relationship between structural integrity and effective connectivity. Methods: We assessed 23 patients with severe brain injury more than 4 weeks post-onset, leading to DOC or locked-in syndrome, and 14 healthy subjects. We calculated PCI using repeated single pulse TMS coupled with high-density electroencephalography, and used it as a surrogate of effective connectivity. Structural integrity was measured using the global FA, derived from diffusion weighted imaging. We used linear regression modelling to test our hypothesis, and computed the correlation between PCI and FA in different groups. Results: Global FA could predict 74% of PCI variance in the whole sample and 56% in the patients' group. No other predictors (age, gender, time since onset, behavioural score) improved the models. FA and PCI were correlated in the whole population (r = 0.86, p < 0.0001), the patients, and the healthy subjects subgroups. Conclusion: We here demonstrated that effective connectivity correlates with structural integrity in brain-injured patients. Increased structural damage level decreases effective connectivity, which could prevent the emergence of consciousness

Measuring directed functional connectivity in mouse fMRI networks using Granger Causality

Resting-state functional magnetic resonance imaging (rsfMRI) of the mouse brain has revealed the presence of robust functional connectivity networks, including an antero-posterior system reminiscent of the human default network (DMN) and correlations between anterior insular and cingulate cortices recapitulating features of the human “salience network”. However, rsfMRI networks are typically identified using symmetric measurements of correlation that do not provide a description of directional information flow within individual network nodes. Recent progress has allowed the measure of directed maps of functional connectivity in the human brain, providing a novel interpretative dimension that could advance our understanding of the brains’ functional organization. Here, we used Granger Causality (GC), a measure of directed causation, to investigate the direction of information flow within mouse rsfMRI networks characterized by unidirectional (i.e. frontal-hippocampal) as well as reciprocal (e.g. DMN) underlying connectional architecture. We observed robust hippocampal-prefrontal dominant connectivity along the direction of projecting ventro-subicular neurons both at single subject and population level. Analysis of key DMN nodes revealed the presence of directed functional connectivity from temporal associative cortical regions to prefrontal and retrosplenial cortex, reminiscent of directional connectivity patterns described for the human DMN. We also found robust directional connectivity from insular to prefrontal areas. In a separate study, we reproduced the same directional connectivity fingerprints and showed that mice recapitulating a mutation associated to autism spectrum disorder exhibited reduced or altered directional connectivity. Collectively, our results document converging directional connectivity towards retrosplenial and prefrontal cortical areas consistent with higher integrative functions subserved by these regions, and provide a first description of directional topology in resting-state connectivity networks that complements ongoing research in the macroscale organization of the mouse brain

Neuronal and behavioral correlates of the influence of contextual cues on value-based decision making = Die neuronalen und behavioralen Korrelate des Einflusses von kontextuellen Reizen auf das wertebasierte Entscheidungsverhalten : Kumulative Arbeit

Value-based decisions are almost omnipresent in life. However, they are not very well understood. Previous research has shown that the decision-making process is dynamic, and can be influenced, for instance, by increasing the saliency of a certain attribute. Therefore, one could hypothesize that it is possible to make decision-makers aware of certain long-term attributes in order to improve decisions. In four main studies published during my doctoral work, I investigated the role of contextual attributes on value-based decision making and how they influence dietary choices. The text presented here puts the published studies into a broader context and reviews various topics, such as the influence of attention on decision making, neuroscientific evidence as well as computational models in decision-making research

Near Infrared Spectroscopy and Electroencephalography For an Assessment of Brain Function in patients with Disorders of Consciousness

There is growing evidence that some of the patients presenting with the Vegetative State (VS), also known as Unresponsive Wakefulness State, can respond to environmental stimuli. This response can be detected by using functional brain imaging, including electroencephalography (EEG) or Near Infrared Spectroscopy (NIRS). By definition, the VS patients are awake but not aware, unlike the patients in the Minimally Conscious State (MCS), who have some fluctuating awareness. Since consciousness is impaired in both conditions, these states are also referred as Disorders of Consciousness (DOC) or prolonged Disorders of Consciousness (pDOC) This thesis aims to develop a bedside applicable tool using the EEG and NIRS for brain function assessment in VS and MCS patients. In this study, two experimental protocols have been developed and validated on healthy subjects. The results showed that using the motor imagery and own subject name stimuli, some of the VS patients were able to wilfully modulate their brain activity in response to those stimuli. The results presented in this thesis can be implemented as a part of a protocol for brain function assessment in pDOC patients and can be used for the further studies for better understanding of the brain function in these patients

EEG-based effective and functional connectivity for differentiating and predicting altered states of consciousness

How does the brain sustain consciousness? In this thesis, and in the work leading up to it, we provide new computational evidence for the importance of the posterior hot zone on one hand, and for long-distance frontoparietal connectivity on the other, in explaining the contrast between loss of consciousness and in maintaining conscious responsiveness. We adopt a factorial approach in our study, crossing two altered states of consciousness with two analytical methods for measuring changes in brain associated with these altered states. Specifically, we study healthy controls under propofol-anaesthesia and patients suffering from disorders of consciousness (DoC), employing functional and effective electroencephalographic (EEG) connectivity, thereby forming a 2-by-2 study design. We first demonstrate the power of functional EEG connectivity for predicting anaesthetic states in the healthy brain, by building a single multivariate regression model combining phase-lag brain connectivity and behaviour- and power-based dependent measures. We show that baseline alpha- and beta-connectivity, as measured prior to an anaesthetic induction, can predict both behaviour- and power-based measures during the induction and peak unresponsiveness, specifically as measured from the posterior electrodes. Next, we study patients suffering from DoC and show that the alpha-band functional connectivity over the left hemisphere, and graph-theoretic network centrality on the right, significantly predict the patient's clinical diagnosis. Our findings suggest a dissociation between mean spectral connectivity and network properties. Building on these findings, we then turn to dynamic causal modelling (DCM) to estimate modulations in effective brain connectivity due to anaesthesia, in and between the default mode network (DMN), the salience network (SAL), and the central executive network (CEN). Advancing current understanding of anaesthetic-induced LOC, we show evidence for a selective breakdown in the posterior hot zone and in medial feedforward frontoparietal connectivity within the DMN, and of parietal inter-network connectivity linking DMN and CEN. In a novel DCM-based out-of-sample cross-validation, we establish the predictive validity of our models, specifically highlighting frontoparietal connectivity as a generalisable predictor of states of consciousness. Importantly, we demonstrate a generalisation of this predictive power in an unseen dataset from the post-anaesthetic recovery state. Finally, we again use DCM to investigate changes in the effective connectivity between DoC patients and healthy controls within the DMN. Specifically, we show that the key difference between healthy controls or conscious patients and completely unresponsive patients is a reduction in left-hemispheric backward frontoparietal connectivity. Finally, with out-of-sample cross-validation, we show that left-hemispheric frontoparietal connectivity can not only distinguish patient groups from each other, it can also generalise to an unseen data subset collected from seemingly unresponsive patients who show evidence of consciousness when assessed with functional neuroimaging. This suggests that effective EEG connectivity can be used to identify covertly aware patients who seem behaviourally unresponsive. Overall, this thesis provides novel insights into the brain dynamics underlying transitions between altered states of consciousness and highlights the value of tracking these dynamics in a clinical context. DCM, though computationally more expensive, can accurately predict states of consciousness and provide causal explanations of the brain dynamics that cannot be inferred from functional connectivity alone. Functional connectivity, though correlational, is still an accurate predictive tool of altered states of consciousness. With clinically challenging, ambiguous cases like potentially covertly aware patients, we propose that the causal explanations and accurate predictions of DCM modelling could outweigh the computational complexity