I have a basic understanding of functional connectivity, but I would like to understand it at a deeper level. What computations are used to assess connectivity and networks, etc.? Also, what are some key differences between EEG and MRI functional connectivity? Do they provide us with comparable information? Or are they significantly different as in ERP vs fMRI?
Early Peer Rejection
I imagine in some cases this may function as a sort of feedback loop (Ladd, 2006). Some children may be more sensitive to or aroused by inputs from the environment. This may look like hyperactivity in the amygdala and/or reduced top-down control from prefrontal cortical structures. Due to their sensitivity to the environment, they may be less likely to approach novelty, including new social situations (Fox, Snidman, Haas, Degnan, & Kagan, 2015). This output behavior impacts their environment as peers will be more likely to form relationships with other children who are engaging in new social situations. The sensitive child may recognize these signs of impending exclusion which may again elicit internal responses that may exacerbate withdrawal outputs. As the cycle continues, the sensitive child may miss out on important foundational childhood social experiences and thus be slower to recognize or act upon social cues setting them on a course that may eventually lead to peer rejection.
I would like to understand more about how we learn new vocabulary, both in childhood and adulthood. Regarding inputs, we have different aspects of word forms. In general, we have orthographic, phonological, and semantic information of each word. However, we may acquire these words in different ways. For example, we may learn these words in formal classroom setting (i.e., explicit learning), or simply by overhearing some conversations (i.e., implicit learning). Regarding outputs, it matters whether we can spell, pronounce the word, as well as whether we can articulate the meaning. However, as psycholinguistic studies have revealed, whether the recently learnt words can interact dynamically with existing lexicons is another important output of word learning. For example, after you learn a new word, you may know its spelling, pronunciation and meaning pretty well. However, this novel word may be still isolated in your lexical network. Lastly, the computations involved is the most intriguing but also unknown part. Relating to my question of interest, previous research has suggested that the initial encoding of the novel word information happens in the hippocampus. After a period of offline consolidation, the information gets transferred to neocortex as a place for long-term storage. It is also argued that only after this offline consolidation stage, the new lexicon can interact dynamically with existing lexical network.
I take a constructionist approach to emotion (see Lindquist et al., 2012), and would love to better understand the biological and neural functions involved in emotional experience. Emotion involves both internal and external inputs. Externally, an individual observes the social environment (including others’ facial expressions and words) and non-social environment (threats, appetitive stimuli, etc.). They also perceive external stimuli such as sounds, sights, smells, etc. in the environment which may be emotionally evocative (e.g., a bad smell may evoke disgust). Internal inputs include interoceptive sensations such as pain, temperature, hunger, valence, and signals of arousal such as increased heartbeat and respiration. Internal inputs also include nonphysiological inputs like personal learning history with an emotion or emotion-evoking experience (e.g., past fear conditioning) and conceptual knowledge about emotion drawn from cultural experience, norms, language used to describe an internal experience, etc. These internal and external inputs interact in iterative feedback loops to produce an emotional experience such as anger, disgust, etc., as individuals map their experiences to known emotion concepts. Outputs include facial expressions, motor impulses (e.g., approach/avoid/freeze), and also perhaps intensification of the emotion if feeling—say– anger makes one angrier.
Rage/extreme anger
Addictive behavior
For example, an alcohol addict happens to be exposed to an alcohol-related cue in his daily life, such as seeing an alcohol shop in the street (inputs). This alcohol cue quickly induces enhanced activations in his prefrontal cortex mediated by glutamate, and his mesolimbic system (e.g., striatum, amygdala) mediated by dopamine, thereby stimulating strong craving for alcohol in him. In addition, the addict also experiences difficulties in inhibiting his alcohol-seeking impulses due to a suppressed GABA-mediated activity in the prefrontal cortex (computations). These neuroadaptations are a long-term consequence of his exposure to alcohol. As a result, the addict goes into the alcohol shop and buys a dozen beers, and finishes them in that single night at home (outputs), although he just decided to abstain from drinking. This addict cycle prevents him from successful alcohol cessation and maintains his addictive behaviors.
My lab studies the intersections of neurodegeneration, emotion, and cognition. In one of the populations that we study, Multiple Sclerosis, the incidence rate of depression is around 50% which is much higher than the general population, and also higher than in other populations with chronic illness. One of the mental states that I’m interested in understanding more is depression in this specific population. Main inputs for this mental state include the practical and functional difficulties of living with a debilitating illness which can obviously contribute to a depressive state. Another potential input for this is the neurodegeneration from the disease and how can detrimentally impact limbic and other potential emotion regulation systems in the brain. One of the currently asked questions is how these effects intersect and why some individuals may have the same inputs but different outcomes. The computational piece is still very much up for debate. Our lab studies individual differences that may make individuals more or less vulnerable to depression after an MS diagnosis. From an information processing framework, we could consider each individual’s computations being affected by things such as existing social support or cognitive reserve. For some individuals, these inputs from having MS can lead to depression which causes typical mental outputs such as sadness, fatigue, irritability, lack of motivation, difficulty concentrating. These can then cause further behavioral outputs such as spending most of one’s free time at home alone or having difficulties at work which have larger implications for a person’s well-being. Also some of these outputs are also symptoms of MS generally, without depression. Disentangling the effect of neurodegeneration versus the added effect of depression on mood and cognition is something I hope to better understand.
Emotion regulation.
From an information processing perspective, regulation aids in attention focusing and inhibition of distractors. This is especially valuable to children’s transition from preschool to kindergarten, where they are not only learning new concepts but also must adapt to a new environment/routine in the classroom. I think we need to consider factors that may constrain the range of development for a regulatory network, such as temperamental reactivity and regulation, which we believe are mostly constitutional or biologically based. Additionally, we need to take into consideration how early environment, namely parenting, can shape the extent to which this network and its interconnectivity develop effectively. Finally, the previous factors may interact and influence each other for continuity or discontinuity in behavioral expression of this regulation neural network. Furthermore, these behavioral expressions (e.g., regulated vs. unregulated behavior) can reinforce the child’s environment (e.g., gaining more friends or further isolation from peers). Based on the limited knowledge I have, I think the main computational model involved is the crosstalk between limbic system and fronto-parietal networks, which I am interested in diving into for my research work here at PSU.
Nonsuicidal self-injury emerges and is maintained by a number of biological and environmental influences. Among the many associated information input sources are cultural or spiritual rituals (one’s own or those inherited from of family), (perceived) stressful situations, or social/environmental influence (e.g., having friends self-injure may induce the desire for someone to do it in order to “fit in”). When these inputs are received, a number of factors influence how it is processed. Biological risk factors (e.g., genetic predisposition, low baseline serotonin levels, trauma-induced HPA dysfunction), social implications (e.g., how concerned is the individual with how others will perceive the act of self-injury), emotional and physiological states (e.g., poor regulation of intense/negative emotions, low endogenous opioid levels, high cortisol levels), and previous experiences (e.g., “last time this helped me calm down”) all come into play in determining whether the information input will translate to engaging in self-injury. In addition to the act of self-harm, other resulting outputs include scars, release of endogenous opioids (reinforcing the behavior biologically), reduction of physiological distress, and potentially effecting change in how the individual is perceived.
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