Communication Students' Skills as a Tool of Development Creativity and Motivation in Geometry

Abstract Often solved problems are problems of students' motivation in the process of teaching and learning. Some authors see the solution in creation a more space to students' creativity in teaching and learning. It is the aim of modern pedagogic and humanistic education, too. The submitted study aims to present possibility of how to teach geometric constructions in connection with real life tasks. The topic Geometric constructions give us space to teach mathematics in interesting way and offer students to be creative. The creative tasks are those tasks which are unknown for students and their content is surprising and nontraditional. We will prepare lesson activities according to official Slovak document entitled National Program of Education. Communication skills and ability to collaborate of students' will be factors of their success in the prepared lesson. Students have to solve problems where do not exist one solution and their content relates to interdisciplinary between geometry and fine arts. For evaluation of students' solutions will be used an implicative analysis with statistical software C.H.I.C. (Classification Hérarchique Implicative et Cohésitive)

Individualized prediction of psychosis in subjects with an at-risk mental state

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition

Learning to trust: social feedback normalizes trust behavior in first-episode psychosis and clinical high risk

Background Psychosis is characterized by problems in social functioning that exist well before illness onset, and in individuals at clinical high risk (CHR) for psychosis. Trust is an essential element for social interactions that is impaired in psychosis. In the trust game, chronic patients showed reduced baseline trust, impaired response to positive social feedback, and attenuated brain activation in reward and mentalizing areas. We investigated whether first-episode psychosis patients (FEP) and CHR show similar abnormalities in the neural and behavioral mechanisms underlying trust. Methods Twenty-two FEP, 17 CHR, and 43 healthy controls performed two trust games, with a cooperative and an unfair partner in the fMRI scanner. Region of interest analyses were performed on mentalizing and reward processing areas, during the investment and outcome phases of the games. Results Compared with healthy controls, FEP and CHR showed reduced baseline trust, but like controls, learned to trust in response to cooperative and unfair feedback. Symptom severity was not associated with baseline trust, however in FEP associated with reduced response to feedback. The only group differences in brain activation were that CHR recruited the temporo-parietal junction (TPJ) more than FEP and controls during investment in the unfair condition. This hyper-activation in CHR was associated with greater symptom severity. Conclusions Reduced baseline trust may be associated with risk for psychotic illness, or generally with poor mental health. Feedback learning is still intact in CHR and FEP, as opposed to chronic patients. CHR however show distinct neural activation patterns of hyper-activation of the TPJ

Dysfunctional insular connectivity during reward prediction in patients with first-episode psychosis

Background: Increasing evidence indicates that psychosis is associated with abnormal reward processing. Imaging studies in patients with first-episode psychosis (FEP) have revealed reduced activity in diverse brain regions, including the ventral striatum, insula and anterior cingulate cortex (ACC), during reward prediction. However, whether these reductions in local brain activity are due to altered connectivity has rarely been explored. Methods: We applied dynamic causal modelling and Bayesian model selection to fMRI data during the Salience Attribution Task to investigate whether patients with FEP showed abnormal modulation of connectivity between the ventral striatum, insula and ACC induced by rewarding cues and whether these changes were related to positive psychotic symptoms and atypical antipsychotic medication. Results: The model including reward-induced modulation of insula-ACC connectivity was the best fitting model in each group. Compared with healthy controls (n = 19), patients with FEP (n = 29) revealed reduced right insula-ACC connectivity. After subdividing patients according to current antipsychotic medication, we found that the reduced insula-ACC connectivity relative to healthy controls was observed only in untreated patients (n = 17), not in patients treated with antipsychotics (n = 12), and that it correlated negatively with unusual thought content in untreated patients with FEP. Limitations: The modest sample size of untreated patients with FEP was a limitation of our study. Conclusion: This study indicates that insula-ACC connectivity during reward prediction is reduced in untreated patients with FEP and related to the formation of positive psychotic symptoms. Our study further suggests that atypical antipsychotics may reverse connectivity between the insula and the ACC during reward prediction

Insular volume abnormalities associated with different transition probabilities to psychosis

Background Although individuals vulnerable to psychosis show brain volumetric abnormalities, structural alterations underlying different probabilities for later transition are unknown. The present study addresses this issue by means of voxel-based morphometry (VBM). Method We investigated grey matter volume (GMV) abnormalities by comparing four neuroleptic-free groups: individuals with first episode of psychosis (FEP) and with at-risk mental state (ARMS), with either long-term (ARMS-LT) or short-term ARMS (ARMS-ST), compared to the healthy control (HC) group. Using three-dimensional (3D) magnetic resonance imaging (MRI), we examined 16 FEP, 31 ARMS, clinically followed up for on average 3 months (ARMS-ST, n=18) and 4.5 years (ARMS-LT, n=13), and 19 HC. Results The ARMS-ST group showed less GMV in the right and left insula compared to the ARMS-LT (Cohen's d 1.67) and FEP groups (Cohen's d 1.81) respectively. These GMV differences were correlated positively with global functioning in the whole ARMS group. Insular alterations were associated with negative symptomatology in the whole ARMS group, and also with hallucinations in the ARMS-ST and ARMS-LT subgroups. We found a significant effect of previous antipsychotic medication use on GMV abnormalities in the FEP group. Conclusions GMV abnormalities in subjects at high clinical risk for psychosis are associated with negative and positive psychotic symptoms, and global functioning. Alterations in the right insula are associated with a higher risk for transition to psychosis, and thus may be related to different transition probabilitie

Detecting the Psychosis Prodrome Across High-Risk Populations Using Neuroanatomical Biomarkers

To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tool

Pituitary gland volume in at-risk mental state for psychosis: a longitudinal MRI analysis

Introduction: Pituitary enlargement has been reported in individuals with schizophrenic psychosis or an at-risk mental state for psychosis (ARMS). In a previous study, our group could show pituitary volume increase in first episode and ARMS patients with later transition to psychosis (ARMS-T). However, there are no longitudinal studies on this issue so far. We therefore examined longitudinally whether transition to psychosis would be accompanied by a further increase of pituitary volume in antipsychotic-naive ARMS patients. METHODS: Magnetic resonance imaging (MRI) data were acquired from 23 antipsychotic-naive individuals with an ARMS. Ten subjects developed psychosis (ARMS-T) and 13 did not (ARMS-NT). ARMS-T were re-scanned after the onset of psychosis, and ARMS-NT were re-scanned at the end of the study period. RESULTS: There was no significant difference of the pituitary volume between ARMS-T and ARMS-NT in our sample, and there were no significant pituitary volume changes over time. Discussion Longitudinally, we could not detect any further volumetric changes in the pituitary volume with transition to psychosis. CONCLUSIONS: This, together with the result of our previous study, could indicate that the perceived level of stress in ARMS patients is constantly high from very early onward

Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition

BACKGROUND: The at-risk mental state for psychosis (ARMS) and the first episode of psychosis have been associated with structural brain abnormalities that could aid in the individualized early recognition of psychosis. However, it is unknown whether the development of these brain alterations predates the clinical deterioration of at-risk individuals, or alternatively, whether it parallels the transition to psychosis at the single-subject level. METHODS: We evaluated the performance of an magnetic resonance imaging (MRI)-based classification system in classifying disease stages from at-risk individuals with subsequent transition to psychosis (ARMS-T) and patients with first-episode psychosis (FE). Pairwise and multigroup biomarkers were constructed using the structural MRI data of 22 healthy controls (HC), 16 ARMS-T and 23 FE subjects. The performance of these biomarkers was measured in unseen test cases using repeated nested cross-validation. RESULTS: The classification accuracies in the HC vs FE, HC vs ARMS-T, and ARMS-T vs FE analyses were 86.7%, 80.7%, and 80.0%, respectively. The neuroanatomical decision functions underlying these discriminative results particularly involved the frontotemporal, cingulate, cerebellar, and subcortical brain structures. CONCLUSIONS: Our findings suggest that structural brain alterations accumulate at the onset of psychosis and occur even before transition to psychosis allowing for the single-subject differentiation of the prodromal and first-episode stages of the disease. Pattern regression techniques facilitate an accurate prediction of these structural brain dynamics at the early stage of psychosis, potentially allowing for the early recognition of individuals at risk of developing psychosis