Unraveling Brain Functional Connectivity of encoding and retrieval in the context of education

peer reviewedHuman memory is an enigmatic component of cognition which many researchers have attempted to comprehend. Accumulating studies on functional connectivity see brain as a complex dynamic unit with positively and negatively correlated networks in perfect coherence during a task. We aimed to examine coherence of network connectivity during visual memory encoding and retrieval in the context of education. School Educated (SE) and College Educated (CE) healthy volunteers (n = 60) were recruited and assessed for visual encoding and retrieval. Functional connectivity using seed to voxel based connectivity analysis of the posterior cingulate cortex (PCC) was evaluated. We noticed that there were reciprocal dynamic changes in both dorsolateral prefrontal cortex (DLPFC) region and PCC regions during working memory encoding and retrieval. In agreement with the previous studies, there were more positively correlated regions during retrieval compared to encoding. The default mode network (DMN) networks showed greater negative correlations during more attentive task of visual encoding. In tune with the recent studies on cognitive reserve we also found that number of years of education was a significant factor influencing working memory connectivity. SE had higher positive correlation to DLPFC region and lower negative correlation to DMN in comparison with CE during encoding and retrieval

Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy

Objectives Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state “epilepsy networks,” we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. Methods Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain “rsfMRI epilepsy networks.” Results SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. Conclusions IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these “rsfMRI epilepsy networks” could reflect epileptogenesis in TLE

Neuropsychological assessment of aggressive offenders: a Delphi consensus study

ObjectiveThis study explores the intricate relationship between cognitive functioning and aggression, with a specific focus on individuals prone to reactive or proactive aggression. The purpose of the study was to identify important neuropsychological constructs and suitable tests for comprehending and addressing aggression.MethodsAn international panel of 32 forensic neuropsychology experts participated in this three-round Delphi study consisting of iterative online questionnaires. The experts rated the importance of constructs based on the Research Domain Criteria (RDoC) framework. Subsequently, they suggested tests that can be used to assess these constructs and rated their suitability.ResultsThe panel identified the RDoC domains Negative Valence Systems, Social Processes, Cognitive Systems and Positive Valence Systems as most important in understanding aggression. Notably, the results underscore the significance of Positive Valence Systems in proactive aggression and Negative Valence Systems in reactive aggression. The panel suggested a diverse array of 223 different tests, although they noted that not every RDoC construct can be effectively measured through a neuropsychological test. The added value of a multimodal assessment strategy is discussed.ConclusionsThis research advances our understanding of the RDoC constructs related to aggression and provides valuable insights for assessment strategies. Rather than suggesting a fixed set of tests, our study takes a flexible approach by presenting a top-3 list for each construct. This approach allows for tailored assessment to meet specific clinical or research needs. An important limitation is the predominantly Dutch composition of the expert panel, despite extensive efforts to diversify