Introduction: Campus life tends to make social and academic demands on college students. To cope with these demands, students are required to use their neurocognitive skills of problem- solving and planning intentional actions that target towards adaptation to college. This paper presents an illuminating perspective that would inform understanding of a new approach to cognitive neuroscience. The linkage between cognition and adaptation was sought in the context of a cognitive neurodynamic approach proposed by the Intention, Meaning, and Perception (IMP) model of neuro-occupation. 
Methods: An ex post facto study was conducted on a convenience sample of 187 college students in Shiraz, Iran. A brief questionnaire was developed to screen participants for diversity of cognitive neurodynamic processing capacity and three standardized questionnaires were used to gather data about college adaptation manifestations. The partial correlation, 1-way, and 2-way ANOVA tests were used to analyze the data. 
Results: The partial correlation test showed large, positive correlation (r≥0.7, P<0.001) between elements of the cognitive neurodynamic process, denoting that the interrelated connections among intention, meaning, and perception were governed by feedback loops. One-way ANOVA test revealed that students with diverse cognitive neurodynamic processing capacity had a variety of college adaptation manifestations. Two-way ANOVA showed a statistically significant main effect for neurodynamic processing capacity (F2, 178=8.1, P<0.001). 
Conclusion: College adaptation could have been established by the cognitive neurodynamic process proposed by the IMP model. Therefore, it is advisable for faculty, mental health practitioners, and counselors who work with students at universities to understand this process and address students’ maladaptation to campus life.

Introduction: Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies have extracted invaluable information from the underlying substrate of the brain. However, cognitive deficiency has insufficiently been assessed by researchers in multiple sclerosis (MS). Therefore, extracting the brain network differences among relapsing-remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study. 
Methods: In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information based on the automated anatomical labeling atlas (AAL). Subsequently, a statistical test was carried out to determine the variation in brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with the Fisher score as a hybrid feature selection method. 
Results: The results of the classification performance measures showed that the construction of a brain network using a new nonlinear connectivity measure in task-fMRI performs better than the linear connectivity measures in terms of classification. The Wilcoxon rank-sum test also demonstrated a superior result for clinical applications.
Conclusion: We believe that non-linear connectivity measures, like KMI, outperform linear connectivity measures, like correlation coefficient in finding the biomarkers of MS disease according to classification performance metrics.
 

Introduction: Methamphetamine use disorder (MUD) has substantial societal and individual implications, necessitating a comprehensive understanding of its neural underpinnings for effective intervention. Key to addiction is the amygdala, implicated in emotion processing and reward systems, which interacts with the prefrontal cortex in addictive behaviors.
Methods: We conducted a study involving 54 male individuals with MUD (age range: 22–44 years) to examine amygdala-cortical connectivity during methamphetamine cue reactivity, aiming to uncover effective neural pathways. We combined generalized psychophysiological interaction (gPPI) analysis and dynamic causal modeling (DCM) to elucidate connectivity dynamics and effective neural pathways. We delved deeper into neuro-behavioral connections using the Pearson correlation and group factor analysis (GFA).
Results: Our findings revealed increased functional connectivity within the amygdala-posterior cingulate cortex (PCC) and amygdala-dorsolateral prefrontal cortex (dlPFC) networks during methamphetamine cue reactivity. DCM revealed a neural network characterized by positive bidirectional connections among the amygdala, dlPFC, and PCC, along with negative intrinsic connections. Interestingly, we observed that the intrinsic self-inhibition of the dlPFC was negatively correlated with post-task positive affect, suggesting its role in emotional regulation. Nonetheless, utilizing GFA, we did not discover any noteworthy cross-unit latent factors between the neural group and variables related to behavior, psychology, or demographics.
Conclusion: These discoveries enrich our comprehension of the neural mechanisms at play in methamphetamine cue reactivity and addiction-related processes. The increased amygdala-cortical connectivity underscores the role of these networks in drug cue processing, potentially contributing to craving and relapse. Effective connectivity analysis highlights the interconnectedness of the amygdala, dlPFC, and PCC, revealing potential pathways for neural signaling during cue reactivity. Our results contribute to the growing body of knowledge about addiction’s neurobiological basis, offering insights that may inform targeted interventions to mitigate the impact of methamphetamine cue reactivity on addiction progression.