Transition from school to university can cause concern for many students. One issue is the gap between students' prior expectations and the realities of university life, which can cause significant ...distress, poor academic performance and increased drop-out rates if not managed effectively. Research has shown several similarities in the expectations of staff and students in regards to which factors determine academic success, but there is also evidence of dissonance. For example, staff consider independent study and critical evaluation as key factors, whereas students view feedback on drafts of work and support from staff as being most important. The aim of the current study was to determine what expectations students hold when starting university education, and what expectations university lecturers have of students entering university. Lecturers (
= 20) and first year students (
= 77) completed a series of questionnaires concerning their expectations of learning in HE (staff and students) and their approach to teaching (staff). Results revealed that students have largely realistic expectations of university. For example, the majority expected to be in charge of their own study. Some unrealistic expectations were also evident, e.g., most expected that teaching would be the same at university as it had been at school. The expectation that lecturers would provide detailed notes varied as a function of student age. Lecturers reported modifying their expectations of students and adapting their teaching approach according to year of study. Information-transmission/teacher-focused style was more common when teaching 1st year students; a more concept-changing/student-focused approach tended to be used when teaching 2nd year students (and above). Lecturer's expectations of student engagement did not differ according to year. Less experienced lecturers reported more negative expectations of student engagement than did experienced lecturers. In line with previous work, we observed overlap in expectations of staff and students, but some clear differences too.
Abstract Background Major Depressive Disorder (MDD) is highly prevalent and potentially devastating, with widespread aberrations in brain activity. Thalamocortical networks are a potential candidate ...marker for psychopathology in MDD, but have not yet been thoroughly investigated. Here we examined functional connectivity between major cortical areas and thalamus. Method Resting-state fMRI from 54 MDD patients and 40 healthy controls were collected. The cortex was segmented into six regions of interest (ROIs) consisting of frontal, temporal, parietal, and occipital lobes and pre-central and post-central gyri. BOLD signal time courses were extracted from each ROI and correlated with voxels in thalamus, while removing signals from every other ROI. Results Our main findings showed that MDD patients had predominantly increased connectivity between medial thalamus and temporal areas, and between medial thalamus and somatosensory areas. Furthermore, a positive correlation was found between thalamo-temporal connectivity and severity of symptoms. Limitations Most of the patients in this study were not medication naïve and therefore we cannot rule out possible long-term effects of antidepressant use on the findings. Conclusion The abnormal connectivity between thalamus and temporal, and thalamus and somatosensory regions may represent impaired cortico-thalamo-cortical modulation underlying emotional, and sensory disturbances in MDD. In the context of similar abnormalities in thalamocortical systems across major psychiatric disorders, thalamocortical dysconnectivity could be a reliable transdiagnostic marker.
Impulsivity is regarded as a multifaceted construct that comprises two dimensions: rapid-response impulsivity and reward-delay impulsivity. It is unclear, however, which aspects of trait impulsivity, ...as assessed by self-report measures are related to rapid-response impulsivity and/or to reward-delay impulsivity, as different results have been reported in studies using both self-report and cognitive measures. This study aimed to directly relate self-report measures of impulsivity to cognitive measures of impulsivity in individuals at low- or high-levels on two impulsivity dimensions, specifically rapid-response impulsivity and reward-delay impulsivity. Participants were classified into high- or low-impulsivity groups based on (1) level of rapid-response impulsivity (determined by BIS-11 Motor subscale scores); (2) level of reward-delay impulsivity (determined by BIS/BAS subscale scores); and (3) a combination of rapid-response impulsivity and reward-delay impulsivity levels. Impulsivity was assessed using Go/No-Go, Stop-Signal and Delay-Discounting tasks and self-report measures. The high rapid-response impulsivity group showed significantly higher reward-delay impulsivity on both, the Delay-Discounting tasks and on self-report measures assessing reward-delay impulsivity, than the low-risk group. Based on the level of reward-delay impulsivity, the high reward-delay impulsivity group scored significantly higher on task-based (cognitive) and self-report measures assessing rapid-response inhibition than the low reward-delay impulsivity group. Combining both dimensions of impulsivity showed that the high-impulsivity group performed significantly worse in rapid-response paradigms and temporally discounted significantly more impulsively than the low-impulsivity group. Thus, combined impulsivity factors provide better assessment of impulsivity than each dimension alone. In conclusion, robust differences in impulsivity can be identified in non-clinical young adults.
Objective
To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders.
Methods
A systematic review following PRISMA guidelines was conducted. A meta‐analysis ...was then performed to assess neuroimaging‐derived brain age gap in three independent groups: (1) schizophrenia and first‐episode psychosis, (2) major depressive disorder, and (3) bipolar disorder.
Results
A total of 18 papers were included. The random‐effects model meta‐analysis showed a significantly increased neuroimaging‐derived brain age gap relative to age‐matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI 2.32; 3.85; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; 0.53; 3.34; p < 0.01) and major depressive disorder (1.12; 0.41; 1.83; p < 0.01). The brain age gap was larger in older compared to younger individuals.
Conclusion
Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.
Background Bipolar disorder is frequently misdiagnosed as major depressive disorder, delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core ...feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation might therefore help discriminate bipolar from major depressive disorder. Methods Thirty-one depressed individuals—15 bipolar depressed (BD) and 16 major depressed (MDD), DSM - IV diagnostic criteria, ages 18–55 years, matched for age, age of illness onset, illness duration, and depression severity—and 16 age- and gender-matched healthy control subjects performed two event-related paradigms: labeling the emotional intensity of happy and sad faces, respectively. We employed dynamic causal modeling to examine significant among-group alterations in effective connectivity (EC) between right- and left-sided neural regions supporting emotion regulation: amygdala and orbitomedial prefrontal cortex (OMPFC). Results During classification of happy faces, we found profound and asymmetrical differences in EC between the OMPFC and amygdala. Left-sided differences involved top-down connections and discriminated between depressed and control subjects. Furthermore, greater medication load was associated with an amelioration of this abnormal top-down EC. Conversely, on the right side the abnormality was in bottom-up EC that was specific to bipolar disorder. These effects replicated when we considered only female subjects. Conclusions Abnormal, left-sided, top-down OMPFC–amygdala and right-sided, bottom-up, amygdala–OMPFC EC during happy labeling distinguish BD and MDD, suggesting different pathophysiological mechanisms associated with the two types of depression.
Objectives The absence of pathophysiologically relevant diagnostic markers of bipolar disorder (BD) leads to its frequent misdiagnosis as unipolar depression (UD). We aimed to determine whether whole ...brain white matter connectivity differentiated BD from UD depression. Methods We employed a three-way analysis of covariance, covarying for age, to examine whole brain fractional anisotropy (FA), and corresponding longitudinal and radial diffusivity, in currently depressed adults: 15 with BD-type I (mean age 36.3 years, SD 12.0 years), 16 with recurrent UD (mean age 32.3 years, SD 10.0 years), and 24 healthy control adults (HC) (mean age 29.5 years, SD 9.43 years). Depressed groups did not differ in depression severity, age of illness onset, and illness duration. Results There was a main effect of group in left superior and inferior longitudinal fasciculi (SLF and ILF) (all F ≥ 9.8; p ≤ .05, corrected). Whole brain post hoc analyses (all t ≥ 4.2; p ≤ .05, corrected) revealed decreased FA in left SLF in BD, versus UD adults in inferior temporal cortex and, versus HC, in primary sensory cortex (associated with increased radial and decreased longitudinal diffusivity, respectively); and decreased FA in left ILF in UD adults versus HC. A main effect of group in right uncinate fasciculus (in orbitofrontal cortex) just failed to meet significance in all participants but was present in women. Post hoc analyses revealed decreased right uncinate fasciculus FA in all and in women, BD versus HC. Conclusions White matter FA in left occipitotemporal and primary sensory regions supporting visuospatial and sensory processing differentiates BD from UD depression. Abnormally reduced FA in right fronto-temporal regions supporting mood regulation, might underlie predisposition to depression in BD. These measures might help differentiate pathophysiologic processes of BD versus UD depression.
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, ...however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets.
Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues – Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.
•Hippodeep demonstrated high stability of measures across all time-points.•Hippodeep had better agreement with manual segmentations than those of FreeSurfer.•Deep neural network performed better on problematic scans as compared to FreeSurfer.
Background Amygdala-orbitofrontal cortical (OFC) functional connectivity (FC) to emotional stimuli and relationships with white matter remain little examined in bipolar disorder individuals (BD). ...Methods Thirty-one BD (type I; n = 17 remitted; n = 14 depressed) and 24 age- and gender-ratio-matched healthy individuals (HC) viewed neutral, mild, and intense happy or sad emotional faces in two experiments. The FC was computed as linear and nonlinear dependence measures between amygdala and OFC time series. Effects of group, laterality, and emotion intensity upon amygdala-OFC FC and amygdala-OFC FC white matter fractional anisotropy (FA) relationships were examined. Results The BD versus HC showed significantly greater right amygdala-OFC FC ( p ≤ .001) in the sad experiment and significantly reduced bilateral amygdala-OFC FC ( p = .007) in the happy experiment. Depressed but not remitted female BD versus female HC showed significantly greater left amygdala-OFC FC ( p = .001) to all faces in the sad experiment and reduced bilateral amygdala-OFC FC to intense happy faces ( p = .01). There was a significant nonlinear relationship ( p = .001) between left amygdala-OFC FC to sad faces and FA in HC. In BD, antidepressants were associated with significantly reduced left amygdala-OFC FC to mild sad faces ( p = .001). Conclusions In BD, abnormally elevated right amygdala-OFC FC to sad stimuli might represent a trait vulnerability for depression, whereas abnormally elevated left amygdala-OFC FC to sad stimuli and abnormally reduced amygdala-OFC FC to intense happy stimuli might represent a depression state marker. Abnormal FC measures might normalize with antidepressant medications in BD. Nonlinear amygdala-OFC FC–FA relationships in BD and HC require further study.
Aim
Alterations in limbic structures may be present before the onset of serious mental illness, but whether subfield‐specific limbic brain changes parallel stages in clinical risk is unknown. To ...address this gap, we compared the hippocampus, amygdala, and thalamus subfield‐specific volumes in adolescents at various stages of risk for mental illness.
Methods
MRI scans were obtained from 182 participants (aged 12–25 years) from the Canadian Psychiatric Risk and Outcome study. The sample comprised of four groups: asymptomatic youth at risk due to family history of mental illness (Stage 0, n = 32); youth with early symptoms of distress (Stage 1a, n = 41); youth with subthreshold psychotic symptoms (Stage 1b, n = 72); and healthy comparison participants with no family history of serious mental illness (n = 37). Analyses included between‐group comparisons of brain measurements and correlational analyses that aimed to identify significant associations between neuroimaging and clinical measurements. A machine‐learning technique examined the discriminative properties of the clinical staging model.
Results
Subfield‐specific limbic volume deficits were detected at every stage of risk for mental illness. A machine‐learning classifier identified volume deficits within the body of the hippocampus, left amygdala nuclei, and medial‐lateral nuclei of the thalamus that were most informative in differentiating between risk stages.
Conclusion
Aberrant subfield‐specific changes within the limbic system may serve as biological evidence to support transdiagnostic clinical staging in mental illness. Differential patterns of volume deficits characterize those at risk for mental illness and may be indicative of a risk‐stage progression.
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