The Effects of Emotional Experiences on Memory Processing

Neural regions, specifically the amygdala, hippocampus, and prefrontal cortex overlap in functions of emotion and memory, indicating a degree of interrelatedness between the two functions. Lesions in medial temporal lobe regions result in an impairment of memory processes specific to emotional stimuli. Additionally, amygdala activity is increased for all valence memory as opposed to neutral. Arousal levels of high and low valence memories affect the pathway for encoding in the brain, and determine the vividness and episodic detail with which a memory will be recorded. The amygdala-hippocampal network is involved in high arousal memory, while a prefrontal cortex-hippocampal network is involved in low arousal. Because of the different neural pathways, negative memory is better remembered, while positive memory is better known. Males and females display the same abilities in working memory, yet have differing neural pathways. Because males\u27 memory networks are more associated with the prefrontal cortex, they have better cognitive control than females for emotional events. Some suggest that because of the implications of a prefrontal vs. amygdala memory encoding, emotional regulation at the onset may be key to preventing traumatic memories from ever developing. Further research should be done in defining the link between emotional and memory processing, to better understand and provide therapy for various neurological disorders

Cholinergic modulation of cognitive processing: insights drawn from computational models

Acetylcholine plays an important role in cognitive function, as shown by pharmacological manipulations that impact working memory, attention, episodic memory, and spatial memory function. Acetylcholine also shows striking modulatory influences on the cellular physiology of hippocampal and cortical neurons. Modeling of neural circuits provides a framework for understanding how the cognitive functions may arise from the influence of acetylcholine on neural and network dynamics. We review the influences of cholinergic manipulations on behavioral performance in working memory, attention, episodic memory, and spatial memory tasks, the physiological effects of acetylcholine on neural and circuit dynamics, and the computational models that provide insight into the functional relationships between the physiology and behavior. Specifically, we discuss the important role of acetylcholine in governing mechanisms of active maintenance in working memory tasks and in regulating network dynamics important for effective processing of stimuli in attention and episodic memory tasks. We also propose that theta rhythm plays a crucial role as an intermediary between the physiological influences of acetylcholine and behavior in episodic and spatial memory tasks. We conclude with a synthesis of the existing modeling work and highlight future directions that are likely to be rewarding given the existing state of the literature for both empiricists and modelers

Trait anxiety and the neural efficiency of manipulation in working memory

The present study investigates the effects of trait anxiety on the neural efficiency of working memory component functions (manipulation vs. maintenance) in the absence of threat-related stimuli. For the manipulation of affectively neutral verbal information held in working memory, high- and low-anxious individuals (N = 46) did not differ in their behavioral performance, yet trait anxiety was positively related to the neural effort expended on task processing, as measured by BOLD signal changes in fMRI. Higher levels of anxiety were associated with stronger activation in two regions implicated in the goal-directed control of attention--that is, right dorsolateral prefrontal cortex (DLPFC) and left inferior frontal sulcus--and with stronger deactivation in a region assigned to the brain's default-mode network--that is, rostral-ventral anterior cingulate cortex. Furthermore, anxiety was associated with a stronger functional coupling of right DLPFC with ventrolateral prefrontal cortex. We interpret our findings as reflecting reduced processing efficiency in high-anxious individuals and point out the need to consider measures of functional integration in addition to measures of regional activation strength when investigating individual differences in neural efficiency. With respect to the functions of working memory, we conclude that anxiety specifically impairs the processing efficiency of (control-demanding) manipulation processes (as opposed to mere maintenance). Notably, this study contributes to an accumulating body of evidence showing that anxiety also affects cognitive processing in the absence of threat-related stimuli

LARGE-SCALE NEURAL NETWORK MODELING: FROM NEURONAL MICROCIRCUITS TO WHOLE-BRAIN COMPLEX NETWORK DYNAMICS

Neural networks mediate human cognitive functions, such as sensory processing, memory, attention, etc. Computational modeling has been proved as a powerful tool to test hypothesis of network mechanisms underlying cognitive functions, and to understand better human neuroimaging data. The dissertation presents a large-scale neural network modeling study of human brain visual/auditory processing and how this process interacts with memory and attention. We first modeled visual and auditory objects processing and short-term memory with local microcircuits and a large-scale recurrent network. We proposed a biologically realistic network implementation of storing multiple items in short-term memory. We then realized the effect that people involuntarily switch attention to salient distractors and are difficult to distract when attending to salient stimuli, by incorporating exogenous and endogenous attention modules. The integrated model could perform a number of cognitive tasks utilizing different cognitive functions by only changing a task-specification parameter. Based on the performance and simulated imaging results of these tasks, we proposed hypothesis for the neural mechanism beneath several important phenomena, which may be tested experimentally in the future. Theory of complex network has been applied in the analysis of neuroimaging data, as it provides a topological abstraction of the human brain. We constructed functional connectivity networks for various simulated experimental conditions. A number of important network properties were studied, including the scale-free property, the global efficiency, modular structure, and explored their relations with task complexity. We showed that these network properties and their dynamics of our simulated networks matched empirical studies, which verifies the validity and importance of our modeling work in testing neural network hypothesis

Dynamic regulation of interregional cortical communication by slow brain oscillations during working memory

Transiently storing information and mentally manipulating it is known as working memory. These operations are implemented by a distributed, fronto-parietal cognitive control network in the brain. The neural mechanisms controlling interactions within this network are yet to be determined. Here, we show that during a working memory task the brain uses an oscillatory mechanism for regulating access to prefrontal cognitive resources, dynamically controlling interactions between prefrontal cortex and remote neocortical areas. Combining EEG with non-invasive brain stimulation we show that fast rhythmical brain activity at posterior sites are nested into prefrontal slow brain waves. Depending on cognitive demand this high frequency activity is nested into different phases of the slow wave enabling dynamic coupling or de-coupling of the fronto-parietal control network adjusted to cognitive effort. This mechanism constitutes a basic principle of coordinating higher cognitive functions in the human brain

Population-level neural coding for higher cognition

Higher cognition encompasses advanced mental processes that enable complex thinking, decision-making, problem-solving, and abstract reasoning. These functions involve integrating information from multiple sensory modalities and organizing action plans based on the abstraction of past information. The neural activity underlying these functions is often complex, and the contribution of single neurons in supporting population-level representations of cognitive variables is not yet clear. In this thesis, I investigated the neural mechanisms underlying higher cognition in higher-order brain regions with single-neuron resolution in human and non-human primates performing working memory tasks. I aimed to understand how representations are arranged and how neurons contribute to the population code. In the first manuscript, I investigated the population-level neural coding for the maintenance of numbers in working memory within the parietal association cortex. By analyzing intra-operative intracranial micro-electrode array recording data, I uncovered distinct representations for numbers in both symbolic and nonsymbolic formats. In the second manuscript, I delved deeper into the neuronal organizing principles of population coding to address the ongoing debate surrounding memory maintenance mechanisms. I unveiled sparse structures in the neuronal implementation of representations and identified biologically meaningful components that can be directly communicated to downstream neurons. These components were linked to subpopulations of neurons with distinct physiological properties and temporal dynamics, enabling the active maintenance of working memory while resisting distraction. Lastly, using an artificial neural network model, I demonstrated that the sparse implementation of temporally modulated working memory representations is preferred in recurrently connected neural populations such as the prefrontal cortex. In summary, this thesis provides a comprehensive investigation of higher cognition in higher-order brain regions, focusing on working memory tasks involving numerical stimuli. By examining neural population coding and unveiling sparse structures in the neuronal implementation of representations, our findings contribute to a deeper understanding of the mechanisms underlying working memory and higher cognitive functions

Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer's disease

Previous experiments have found that individuals with Alzheimer's disease (AD) show increased activity in prefrontal regions compared with healthy age-matched controls during cognitive tasks. This has been interpreted as compensatory reallocation of cognitive resources, but direct evidence for a facilitating effect on performance has been lacking. To address this we measured neural activity during semantic and episodic memory tasks in mildly demented AD patients and healthy elderly controls. Controls recruited a left hemisphere network of regions, including prefrontal and temporal cortices in both the semantic and episodic tasks. Patients engaged a unique network involving bilateral dorsolateral prefrontal and posterior cortices. Critically, activity in this network of regions was correlated with better performance on both the semantic and episodic tasks in the patients. This provides the most direct evidence to date that AD patients can use additional neural resources in prefrontal cortex, presumably those mediating executive functions, to compensate for losses attributable to the degenerative process of the disease.8 page(s

Mathematics anxiety and cognition : a computational modelling study

Anxiety about performing numerical calculations is becoming an increasingly important issue. Termed mathematics anxiety, this condition negatively impacts performance in numerical tasks which can affect education outcomes and future employment prospects. The disruption account proposes this poor performance is from the anxiety and its worrying thoughts disrupting the limited resources of working memory (specifically the attentional and inhibitory functions) leaving less cognitive resources available for the current task. There are many behavioural studies on mathematics anxiety. However, its underlying cognitive and neural mechanisms remain unclear. This thesis examines the relationship between mathematics anxiety and attentional control using neural network modelling, there are no neural network models simulating mathematics anxiety. The numerical Stroop task and the symbolic number comparison task were modelled with a single neural network model architecture examining the effect of modifications to both tasks. Different model modifications were used to simulate high and low math-anxious conditions by modifying attentional processes and learning. The model simulations suggest that mathematics anxiety is associated with reduced attention to numerical stimuli. These results are consistent with attentional control theory where anxiety decreases the influence of the goal-directed attentional system and increases the influence of the stimulus-driven attentional system. Notably, when simulating the numerical Stroop task, the high math-anxious model with reduced attention to numerical stimuli experienced less neural activation in the response layer for the inhibitory condition than the low math-anxious model, suggesting an under activation of working memory resources when experiencing conflict. Furthermore, the model was able to account for several other cognitive conditions, including reduced learning, the physical Stroop task across learning, and the speed-accuracy trade-off

Executive control: balancing stability and flexibility via the duality of evolutionary neuroanatomical trends

The concept of executive functions has a rich history and remains current despite increased use of other terms, including working memory and cognitive control. Executive functions have sometimes been equated with functions subserved by the frontal cortex, but this adds little clarity, given that we so far lack a comprehensive theory of frontal function. Pending a more complete mechanistic understanding, clinically useful generalizations can help characterize both healthy cognition and multiple varieties of cognitive impairment. This article surveys several hierarchical and autoregulatory control theories, and suggests that the evolutionary cytoarchitectonic trends theory provides a valuable neuroanatomical framework to help organize research on frontal structure-function relations. The theory suggests that paleocortical/ventrolateral and archicortical/dorsomedial trends are associated with neural network flexibility and stability respectively, which comports well with multiple other conceptual distinctions that have been proposed to characterize ventral and dorsal frontal functions, including the “initiation/inhibition,” “what/where,” and “classification/expectation” hypotheses