Introduction: The purpose of this investigatory neuroimaging analysis was done to better  understand the pharmacodynamics of Lithium by isolating the norepinephrine pathway in the brain.  To accomplish this, we compared patients with Bipolar Disorder treated with Lithium to patients  diagnosed with Major Depression or Depressive Disorder who are treated with Selective Serotonin  Reuptake Inhibitors (SSRIs).
Methodology: We used Standardized Low Resolution Brain Electrotomography to calculate  the whole brain, voxel-by-voxel, unpaired t-tests Statistical non-Parametric Maps. For our first  electrophysiological neuroimaging investigation, we compared 46 patients (average age = 34 ±  16.5) diagnosed with Bipolar Affective Disorder to three patient groups all diagnosed with Major  Depression or Depressive Episode. The first is with 48 patients diagnosed with Major Depression or  Depressive Episode (average age = 49 ± 12.9), the second to 16 male depressive patients (average  age = 45 ± 15.1), and the final comparison to 32 depressive females (average age = 50 ± 11.7).
Results: The results of sLORETA three-dimensional statistical non-parametric maps  illustrated that Lithium influenced an increase in neurotransmission in the right Superior Temporal
Gyrus (t=1.403, p=0.00780), Fusiform Gyrus (t=1.26), and Parahippocampal Gyrus (t=1.29).
Moreover, an increased in neuronal function was found was also identified at the Cingulate Gyrus
(t=1.06, p=0.01200).
Conclusion: We are proposing a translational clinical biological marker for patients  diagnosed with Bipolar Disorder to guide physicians during the course of Lithium therapy and have  identified neuroanatomical structures influenced by norepinephrine.

In this retrospective of electroencephalograms were to identify a surrogate biomarker for the  Dopamine D2 receptors in the brain by comparing patients diagnosed with Schizophrenia taking  Atypical Antipsychotics to Depressive patients medicated with Selective Serotonin Reuptake  Inhibitors. To achieve this, thirty-seconds of resting EEG were spectrally transformed in  sLORETA. Three-dimensional statistical non-paramentric maps (SnPM) for the sLORETA Global  Field Power within each band were then computed. Our results illustrated that the Right Superior  Frontal Gyrus (t=2.049, p=0.007), along the dopamine mesolimbic pathway, had higher neuronal  oscillations in the delta frequency band in the 100 Schizophrenia patients as compared to the 32-depressive female patients. The comparisons with both the 48 depressive patient cohort or the  sixteen male depressive patient cohort did not yield any statistically significant findings. We  conclude that the Superior Frontal Gyrus should be investigated as a possible surrogate biomarker  for preclinical and clinical drug discovery in neuropharmacology.

Anorexia Nervosa (AN) is characterized by Diagnostic and Statistical Manual of Mental  Disorders Volume 4 (DSM IV), as one's refusal to maintain a body weight that is above the  calculated limit, which is determined by an algorithm involving one's height and weight. As more
emphasis in society is placed on one's body image and appearance there has been an increase in the  prevalence of this disease. Previously, the sole diagnostic imaging modality was fMRI. Studies  determined that there was reduced blood flood in the Parahippocampal Gyrus, and Left Fusiform  Gyrus, of those afflicted with AN. Electroencephalography (EEG) was utilized as an alternative  imaging modality that was more cost effect. It was determined that the activated regions localized
on the fMRI study coincided with those highlighted on the EEG report and previous fMRI studies.  The goal of this study was to determine a more cost effective way to earlier detect a diagnosis of  AN. The desired outcome would be for patients afflicted with AN to be diagnosed and treated at an  earlier stage, increasing their overall long-term survival.

Logic is a set of well-formed formulae, along with an inference relation. But the Classical  Logic is bivalent; for this reason, very limited to solve problems with uncertainty on the data. It is  well-known that Artificial Intelligence requires Logic. Because its Classical version shows too  many insufficiencies, it is very necessary to introduce more sophisticated tools, as may be Non-
Classical Logics; amongst them, Fuzzy Logic, Modal Logic, Non-Monotonic Logic, Para-consistent  Logic, and so on. All them in the same line: against the dogmatism and the dualistic vision of the  world: absolutely true vs. absolutely false, black vs. white, good or bad by nature, Yes vs. No, 0 vs.
1, Full vs. Empty, etc. We attempt to analyze here some of these very interesting Classical and
modern Non-Classical Logics.

Some theoretical control models posit that the fatigue which is developed during physical  activity is not always peripheral and it is the brain which causes this feeling of fatigue. This fatigue  develops due to a decrease of metabolic resources to and from the brain that modulates physical  performance. Therefore, this research was conducted to find out if there was finite level of
metabolic energy resources in the brain, by performing both mental and physical activities to  exhaustion. It was found that there was an overflow of information during the exercise-involved  experiment. The circular relationship between fatigue, cognitive performance and arousal state  insinuates that one should apply more effort to maintain performance levels which would require  more energy resources that eventually accelerates the development of fatigue. Thus, there appeared  to be a limited amount of energy resources in the brain as shown by the cognitive performance of  the participants.

In this paper, the idea of a new artificial intelligence based optimization algorithm, which is  inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation  algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires  from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a  swarm-oriented evolutional problem solution approach; because it includes many methods related  to elimination of weak swarm members and trying to improve the solution process by supporting  the solution space via new swarm members. In order have better idea about success of the  algorithm; it has been tested via some benchmark functions. At this point, the obtained results show  that the algorithm can be an alternative to the literature in terms of single-objective optimization
solution ways. Vortex Optimization Algorithm (VOA) is the name suggestion by the authors; for  this new idea of intelligent optimization approach.