Serum Serotonin Abnormality in Depression

Background

Serotonin plays an important role in treatment of depression. We evaluated the clinical correlates of plasma serotonin levels in depressed patients before and after treatment.

Methods

Study sample comprised of 40 patients diagnosed on ICD-10 diagnostic criteria, and an equal number of healthy matched controls. Subjects were evaluated on Beck''s Depression Inventory (BDI) and Suicide Ideation Scale (SIS), before and after the treatment. Blood samples were collected from all the cases and controls before starting the antidepressant medication with selective serotonin reuptake inhibitors (SSRI''s). Serum serotonin levels were measured before and after treatment.

Result

Significant differences in scores before and after the intervention on BDI, SIS and serotonin levels of cases and controls (p<.000) were noted. Correlation between the serum serotonin levels before and after the treatment, and between the rating scales did not reveal significant association (p > 0.05). Patients with suicidal intentions had lower levels of serotonin. The scores changed after intervention.

Conclusion

Treatment with SSRI''s had shown significant changes in clinical conditions. However these changes did not relate significantly with serum serotonin levels.Key Words: Serotonin, Depression, Selective serotonin reuptake inhibitors     

Role of the anterior insular cortex in integrative causal signaling during multisensory auditory–visual attention

Coordinated attention to information from multiple senses is fundamental to our ability to respond to salient environmental events, yet little is known about brain network mechanisms that guide integration of information from multiple senses. Here we investigate dynamic causal mechanisms underlying multisensory auditory–visual attention, focusing on a network of right‐hemisphere frontal–cingulate–parietal regions implicated in a wide range of tasks involving attention and cognitive control. Participants performed three ‘oddball’ attention tasks involving auditory, visual and multisensory auditory–visual stimuli during fMRI scanning. We found that the right anterior insula (rAI) demonstrated the most significant causal influences on all other frontal–cingulate–parietal regions, serving as a major causal control hub during multisensory attention. Crucially, we then tested two competing models of the role of the rAI in multisensory attention: an ‘integrated’ signaling model in which the rAI generates a common multisensory control signal associated with simultaneous attention to auditory and visual oddball stimuli versus a ‘segregated’ signaling model in which the rAI generates two segregated and independent signals in each sensory modality. We found strong support for the integrated, rather than the segregated, signaling model. Furthermore, the strength of the integrated control signal from the rAI was most pronounced on the dorsal anterior cingulate and posterior parietal cortices, two key nodes of saliency and central executive networks respectively. These results were preserved with the addition of a superior temporal sulcus region involved in multisensory processing. Our study provides new insights into the dynamic causal mechanisms by which the AI facilitates multisensory attention.     

Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development

Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally oriented attention [central executive network (CEN)] and internally oriented cognition [default mode network (DMN)]. How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared with children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. Diffusion tensor imaging tractography revealed increased structural integrity in adults compared with children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood.     

Cross sectional assessment of empathy among undergraduates from a medical college

Background

Empathy is essentially a desirable quality among clinicians and can be developed during medical education. Studies from outside India have shown that higher empathy is related to better competency and choice of specialty may be related to empathy levels in them. Change in empathy levels among undergraduate medical students with progressive training has been often ascribed to reasons such as curriculum content, timing of clinical rotations. Gender differences in empathy levels also vary among different countries. Since many of such factors differ in India there is a need therefore to understand empathy and its correlates among medical students in India.

Method

A cross sectional study was undertaken in a large medical college among the undergraduates of first, third, fifth, seventh and ninth semesters to measure their empathy levels. The evaluation was done using the Jefferson's Scale for Physician's Empathy- Student version.

Results

The study revealed highest empathy at entry level and a significant fall by seventh semester (p = 0.002). Female students had significantly higher empathy levels than male students (p = 0.012) across all semesters. The variance in empathy scores according specialty chosen is not statistically significant (p = 0.2468).

Conclusion

The progressive decline in empathy levels with years in medical college here is seen much later than in western studies. Female students are more empathetic than male students. The relation of mean empathy scores and choice of specialty is inconclusive and at variance from other studies.     

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on independent component analysis (ICA) for reducing gradient artifacts from spiral in–out and echo-planar pulse sequences at 3 T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen, P., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230–239.), principal component analysis (Niazy, R., Beckmann, C., Iannetti, G., Brady, J., Smith, S., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720–737.), Taylor series ( Wan, X., Iwata, K., Riera, J., Kitamura, M., Kawashima, R., 2006. Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts. Clin. Neurophysiol. 117, 681–692.) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (< 12.5 Hz) with high signal-to-noise ratio (SNR > 4). Our algorithms outperformed all of these methods on resting EEG in the theta and alpha bands (SNR > 4); however, for all methods, signal recovery was modest (SNR  1) in the beta band and poor (SNR < 0.3) in the gamma band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (< 0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3 T using spiral in–out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework.