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The Limbic System
Published in Jay A. Goldstein, Chronic Fatigue Syndromes, 2020
The anatomic localization of the heteromodal areas, which can provide substantial preprocessed sensory information to the motivational areas of the paralimbic cortex, is not well known in general medicine. These areas include the prefrontal cortex, the supramarginal gyrus, the medial parieto-occipital area, the inferior parietal lobule and the angular gyms.
The Central Nervous System Organization of Behavior
Published in Rolland S. Parker, Concussive Brain Trauma, 2016
Brainstem and limbic functions are integrated with cognitive and motor planning. Significant varying temperamental disorders reflect the different motivational biases for motor control reflect input from various sections of the paralimbic cortex. Hemispheric specialization contributes to emotional self-regulation, but the alternative explanation is disinhibition of the contrelateral hemisphere versus disinhibition of ipsilateral subcortical structures. The frontal lobe participates in the arousal system, which explains deficits of motivation and initiative following lesions. Emotional disorders cannot be clearly attributed to a particular lobe (Tucker et al., 1995).
Synthesis, Enzyme Localization, and Regulation of Neurosteroids
Published in Sheryl S. Smith, Neurosteroid Effects in the Central Nervous System, 2003
Higher cortical regions, such as the paralimbic cortex (e.g., orbitofrontal, insular, anterior temporal, and anterior cingulate) and prefrontal cortex, manage more cognitively demanding stimuli. The orbitofrontal cortex is involved in determining the significance of anxietγ- or fear-producing sensory events, the choice and implementation of behaviors important for survival, and the extinction of conditioned fear responses via its reciprocal interactions with the amygdala. The medial prefrontal cortex also has projections to the amygdala that are involved in the suppression of responses to anxietγ-related or fearful stimuli. Overall, the prefrontal cortex, in conjunction with the adjacent orbitofrontal cortex and anterior cingulate, helps regulate the peripheral responses to stress. Thus, these higher cortical regions provide a link between the sensory association, premotor and executive areas, and the limbic system.
Anticipatory, Relief-Oriented and Permissive Beliefs in Patients with Suicidal Behaviors: An Exploratory Case-Control Study
Published in Archives of Suicide Research, 2021
Jonathan Del-Monte, Perluigi Graziani
Firstly, the “emotional processing” will assign a negative valence to this situation. This negative emotion is intense and lasting in time may lead to an inability to associate emotion/affect with behavior, to accurately decode the affective context that guides behavior and to evaluate the consequences of actions regarding the positive or negative outcome. These difficulties may lead some errors in decision-making in real-life situations due to inaccurate interpretation of the positive or negative outcome of a given a choice, leading in some cases to impulsivity (Bechara, Damasio, Damasio, & Anderson, 1994). Locations of the lesions or dysfunctions associated with emotional mechanism are orbital and medial prefrontal cortex and limbic territories of the basal ganglia (ventral striatum, ventral pallidum) (Levy & Dubois, 2006). Patients with risk of suicide, particularly better-planned ones, were associated with deactivation of the lateral prefrontal cortex (lPFC) in response to value difference favoring the immediate option. Impulsivity was associated with an altered paralimbic (precuneus/posterior cingulate cortex [PCC]) encoding of value difference during intertemporal choice. Better-planned suicidal acts were associated with altered PFC representations of value difference (Vanyukov et al., 2016). This study showed blunted learned value signals in the paralimbic cortex (ventromedial prefrontal cortex and PCC/precuneus) of patients with high risk of suicide, particularly those with poorly planned attempts, and individuals high in trait impulsivity during a probabilistic reversal learning task (Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013).