JUST PUBLISHED: Treating Anxiety by Modifying Negative Cognitive Biases

Dr Sirous Mobini and colleagues have recently published a integrative review of the literature investigating the treatment of anxiety using cognitive bias modification.

Cognitive theories of social anxiety indicate that negative biases in thinking play a key role in causing and maintaining social anxiety. On the basis of these cognitive theories, research has shown that individuals with social anxiety interpret ambiguous social situations in a negative (or less passive) manner.

Cognitive Bias Modification for interpretative biases (CBM-I) was developed from this experimental research to reduce these negative interpretative biases in social anxiety. CBM-I intervention is entirely delivered by computer and there is no contact with a clinician. Over several sessions participants are trained to develop positive interpretations of ambiguous social scenarios. Participants read a series of ambiguous social scenarios presented on the computer screen which are ultimately resolved positively via completion of incomplete words (e.g. p--as-nt). All passages are presented in four lines individually and the participants’ task is to complete the word fragment (last word in the passage) after reading the passage by keying the first missing letter (in this example the letter ‘l’ for pleasant.

The results of the literature review have shown that this CBM-I positive training not only reduces negative interpretations of ambiguous social situations but also reduces social anxiety symptoms in individuals with social anxiety. However, the long-term positive effects of CBM-I need to be investigated with patients with social anxiety disorder.

For more information, please see the following journal article:

Mobini, S., Reynolds, S., & Mackintosh, B. (2012). Clinical Implications of Cognitive Bias Modification for Interpretative Biases in Social Anxiety: An Integrative Literature Review Cognitive Therapy and Research, 37 (1), 173-182 DOI: 10.1007/s10608-012-9445-8

or e-mail Dr Mobini at Sirous.Mobini@newcastle.edu.au

Colloquium Presentation, by Dr. Renate Thienel, on side effects of haliperidol vs. risperidone is schizophrenia treatment.

The School of Psychology is proudly hosting a talk by:

 Prof. Renate Thienel

Priority Centre for Translational Neuroscience & Mental Health Research

University of Newcastle

Title: Differential effects of risperidone vs. haloperidol on brain activation during a working memory task in first episode schizophrenia patients.

Date: Thursday 23^rd May 2013, 12-1pm in Keats Reading Room (AVLG17) (video streaming to AV3 in the Ourimbah library)

If you would like to meet with Dr. Thienel, please contact A/Prof Scott Brown (scott.brown@newcastle.edu.au).

Abstract: Neurocognitive impairments in schizophrenia are common and clinically relevant. The majority of people diagnosed with schizophrenia will experience a significant decline in global, social and occupational function levels in the course of their illness. Amongst the different cognitive domains, memory impairments in particular are regarded as possible intermediate phenotypes of schizophrenia. I investigated whether haloperidol and risperidone - two neuroleptic drugs with differential receptor binding profiles - show distinct impacts on functional networks mediating working memory. Differential effects of first- and second-generation antipsychotics on cognition have been reported before, however most of the previous results where employing disproportionately high doses of haloperidol, with the associated increased risk of unwanted side effects, such as cognitive impairment and extrapyramidal motor side effects. I compared the functional haemodynamic response during an n-back task in first-episode schizophrenia patients on a comparably low to moderate dose of haloperidol versus risperidone. Risperidone treated patients showed stronger activations than haliperidol patients in a cortical network that has previously been associated with this type of working memory task. As the results were controlled for medication dose, and neither side effects nor co-treatment differed between the groups, the results are not likely to be affected by these confounding factors, but rather reflect the drugs different receptor binding profile, such as their differential mesocortical dopaminergic input, and pro-cholinergic properties. On the basis of the behavioural results these differences in activation might represent adaptive processes in order to maintain a sufficient level of performance on the one hand, whilst on the other hand patient’s slower behavioural performance might rest upon a failure to recruit task-relevant brain areas comparably.

Bio: Dr Renate Thienel who graduated in Germany (B.A. [Hons.] M.Psych. [Research], Ph.D. [Dr. rer. nat.] in 2007) currently holds a University of Newcastle Postdoctoral Research Fellowship and is based at the Priority Centre for Translational Neuroscience & Mental Health Research. Renate is affiliated with the Schizophrenia Research Institute and the Hunter Medical Research Institute and is an active member of the Australasian Society of Psychiatric Research. Her research focuses on the aetiologies and the rehabilitation of schizophrenia by studying event related potentials, functional magnetic resonance imaging, magnetic resonance spectroscopy, and diffusion tensor imaging. Renate collaborates with national and international colleagues on various neuroimaging research projects into schizophrenia, the prediction of transition to psychosis, the shared biological basis of schizophrenia, a genetically high risk population (22q11DS), and a novel neuro-feedback procedure to modify brain perfusion using functional magnetic resonance imaging with applications for neurocognitive rehabilitation in schizophrenia and dementia as well as cerebral stroke. Her strong translational approach also includes the creation of a normative database of electroencephalographically recorded sensory auditory memory function in children and adolescents with great potential as a diagnostic tool for the detection of “at-risk mental state”.

Colloquium Presentation: Prof. Polina Golland, MIT, on better ways to investigate fMRI data.

Prof. Polina Golland

Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology

Title: Alignment-Free Exploratory Analysis of fMRI Data

Date: Thursday 9^th May 2013, 12-1pm in Keats Reading Room (AVLG17) (video streaming to AV3 in the Ourimbah library)

If you would like to meet with Prof. Golland, please contact A/Prof Scott Brown (scott.brown@newcastle.edu.au).

Abstract: We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. Our motivation comes from visual fMRI studies with increasingly large number of image categories. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI responses. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters. The model eliminates the need for spatial normalization while still enabling us to fuse data from multiple subjects.

If time permits, I will also discuss our current research in
characterizing the spatial variability of activation patterns across
subjects.

(Joint work with Danial Lashkari, Ramesh Sridharan, George Chen,
Ed Vul and Nancy Kanwisher.)

Bio: I am an associate professor in the EECS Department and the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. My primary research interest is in developing novel techniques for image analysis and understanding. I particularly enjoy working on algorithms that either explore the geometry of the world and the imaging process in a new way or improve image-based inference through statistical modeling of the image data. I am interested in shape modeling and representation, predictive modeling and visualization of statistical models. My current research focuses on developing statistical analysis methods for characterization of biological processes using images (from MRI to microscopy) as a source of information. In this domain, I am interested in modeling biological shape and function, how they relate to each other and vary across individuals.