The Department for Sensory and Sensorimotor Systems of the Max-Planck-Institute for Biological Cybernetics studies the processing of sensory information (visual, auditory, tactile, olfactory) in the brain and the use of this information for directing body movements and making cognitive decisions. The research is highly interdisciplinary and uses theoretical and experimental approaches in humans. Our methodologies include visual psychophysics, eye tracking, fMRI, EEG, TMS in humans. For more information, please visit the department website: www.lizhaoping.org We are currently looking for a Lab Mechatronics / Programmer/ Research and Admin Assistant (m/f/d) 100% to join us at the next possible opportunity. The position: You will provide hardware, software, and managerial support for a diverse set of brain and neuroscience research activities. This includes: • Computer and IT support of Windows and Linux systems • Programming and debugging of computer code, especially at the stage of setting up new equipment or new experimental platforms • Provide technical, administrative, and operational support in the research data taking process. (The position holder should have the ability to quickly learn the data taking processes involved in the labs.) • Hardware repairs and troubleshooting • Equipment inventory and maintenance • Supervising and training of new equipment users • Setting up, updating and managing the database of knowledge and data from research projects, personnel and activities Our department is interdisciplinary, with research activities including human visual psychophysics, eye tracking, fMRI, EEG, TMS. We are looking for a person with a broad technical knowledge base, who loves working in a scientific environment and who is curious, open-minded, and able to adapt and learn new skills and solve new problems quickly. The set of skills that the individual should either already have or can quickly learn includes: MATLAB/Psychotoolbox, Python/OpenCV, Julia/OpenGL, Java, graphics and display technologies, EEG equipment and similar, eye tracking, optics, electronics/controllers/sensors, Arduino/Raspberry Pi, etc. We offer: We offer highly interesting, challenging and varied tasks; you will work closely and collaboratively with scientists, students, programmers, administrative staff, and central IT and mechanical/electronic workshop support to help achieve the scientific goals of the department. A dedicated team awaits you in an international environment with regular opportunities for further education and training. The salary is paid in accordance with the collective agreement for the public sector (TVöD Bund), based on qualification and experience and will include social security benefits and additional fringe benefits in accordance with public service provisions. This position is initially limited to two years, with the possibility of extensions and a permanent contract. The Max Planck Society seeks to employ more handicapped people and strongly encourages them to apply. Furthermore, we actively support the compatibility of work and family life. The Max Planck Society also seeks to increase the number of women in leadership positions and strongly encourages qualified women to apply. The Max Planck Society strives for gender equality and diversity. Your application: The position is available immediately and will be open until filled. Preference will be given to applications received by September 30th, 2022. We look forward to receiving your application that includes a cover letter, your curriculum vitae, relevant certificates, and three names and contacts for reference letters electronically by e-mail to jobs.li@tuebingen.mpg.de, where informal inquiries can also be addressed to. Please note that incomplete applications will not be considered. For further opportunities in our group, please visit http://www.lizhaoping.org/jobs.html

A NIH-funded postdoctoral position is immediately available in Dr. Stéphane Maison’s laboratory in the Department of Otolaryngology – Head & Neck Surgery at the Massachusetts Eye & Ear – Harvard Medical School. Our research interests focus on identifying biomarkers of a large range of etiologies and their associated disorders including difficulties hearing and communicating in noisy environments, reduced sound level tolerance and tinnitus using a test battery based on behavioral, electrophysiologic, and psychophysical measures. Salary and benefits are consistent with NIH guidelines and institution policies based on applicant’s experience. Highly motivated candidates who recently graduated with a PhD in biomedical engineering, computational biology, hearing science, neuroscience, or other related fields are welcome to apply. The applicant should have strong programming skills (e.g., Matlab, Python), independent and productive. Experience with human testing is preferred but not required. The fellow will receive an appointment at Massachusetts Eye and Ear and Harvard Medical School. Interested applicants should apply using the following link: https://partners.taleo.net/careersection/mee/jobdetail.ftl?job=3299643&tz=GMT-04%3A00&tzname=America%2FNew_York

A NIH-funded postdoctoral position is immediately available in the Department of Otolaryngology - Head & Neck Surgery at the Massachusetts Eye & Ear - Harvard Medical School. Salary and benefits are consistent with NIH guidelines and institution policies based on applicant's experience. We are seeking highly motivated candidates who recently graduated with a PhD in hearing science, neuroscience, computer sciences or biomedical engineering. The applicant should have programming skills (e.g., Matlab, Python), be motivated, independent, and productive. Experience with human testing is a plus. The fellow will receive an appointment at Massachusetts Eye and Ear and Harvard Medical School. Our research interests focus on identifying biomarkers of cochlear synaptopathy in patients with a large range of sensorineural etiologies.

The Centre for Artificial Intelligence and Neuroscience at University of Bonn is looking to recruit a postdoctoral fellow and a PhD student to undertake high quality research and produce high-impact publications in a collaborative research project investigating the mechanisms of human aversive learning (a pre-clinical model of anxiety disorders) in a big-data approach. The project is led by Prof Dominik Bach. The goal of the advertised positions is to (a) conduct an fMRI mega-analysis of multi-voxel threat representations, using existing data from the ENIGMA consortium, and (b) adapt and improve methods to infer threat learning from psychophysiological and behavioural data in fMRI and ambulatory settings. The position involves a close collaboration with the Department for Imaging Neuroscience (FIL) at UCL Queen Square Institute of Neurology, London, UK, where part of the team is based. The role includes implementing fMRI analysis pipelines in HALFpipe, coordination with participating sites to obtain and process data, mega-analysis of threat representation geometry, implementing and developing psychophysiology and behavioural analysis pipelines using PsPM/Matlab, CogLearn/VRthreat/R, and statistical modelling (e.g. Rstan), and publication of research and development results.

My talk will discuss the use of some of these four, simple Matlab functions to simulate models of timing, and to fit models to empirical data. Feel free to examine the code and the relatively brief book chapter that explains the code before the talk if you would like to learn more about computational/mathematical modeling.

Last in a three-part lecture series about using matlab in neuroimaging. For full details go here: https://bit.ly/matlabimaging

Impulsivity and compulsivity are behavioural traits that underlie many aspects of decision-making and form the characteristic symptoms of Obsessive Compulsive Disorder (OCD) and Gambling Disorder (GD). The neural underpinnings of aspects of reward and avoidance learning under the expression of these traits and symptoms are only partially understood. " "The present study combined behavioural modelling and neuroimaging technique to examine brain activity associated with critical phases of reward and loss processing in OCD and GD. " "Forty-two healthy controls (HC), forty OCD and twenty-three GD participants were recruited in our study to complete a two-session reinforcement learning (RL) task featuring a “probability switch (PS)” with imaging scanning. Finally, 39 HC (20F/19M, 34 yrs +/- 9.47), 28 OCD (14F/14M, 32.11 yrs ±9.53) and 16 GD (4F/12M, 35.53yrs ± 12.20) were included with both behavioural and imaging data available. The functional imaging was conducted by using 3.0-T SIEMENS MAGNETOM Skyra syngo MR D13C at Monash Biomedical Imaging. Each volume compromised 34 coronal slices of 3 mm thickness with 2000 ms TR and 30 ms TE. A total of 479 volumes were acquired for each participant in each session in an interleaved-ascending manner. " " The standard Q-learning model was fitted to the observed behavioural data and the Bayesian model was used for the parameter estimation. Imaging analysis was conducted using SPM12 (Welcome Department of Imaging Neuroscience, London, United Kingdom) in the Matlab (R2015b) environment. The pre-processing commenced with the slice timing, realignment, normalization to MNI space according to T1-weighted image and smoothing with a 8 mm Gaussian kernel. " " The frontostriatal brain circuit including the putamen and medial orbitofrontal (mOFC) were significantly more active in response to receiving reward and avoiding punishment compared to receiving an aversive outcome and missing reward at 0.001 with FWE correction at cluster level; While the right insula showed greater activation in response to missing rewards and receiving punishment. Compared to healthy participants, GD patients showed significantly lower activation in the left superior frontal and posterior cingulum at 0.001 for the gain omission. " " The reward prediction error (PE) signal was found positively correlated with the activation at several clusters expanding across cortical and subcortical region including the striatum, cingulate, bilateral insula, thalamus and superior frontal at 0.001 with FWE correction at cluster level. The GD patients showed a trend of decreased reward PE response in the right precentral extending to left posterior cingulate compared to controls at 0.05 with FWE correction. " " The aversive PE signal was negatively correlated with brain activity in regions including bilateral thalamus, hippocampus, insula and striatum at 0.001 with FWE correction. Compared with the control group, GD group showed an increased aversive PE activation in the cluster encompassing right thalamus and right hippocampus, and also the right middle frontal extending to the right anterior cingulum at 0.005 with FWE correction. " " Through the reversal learning task, the study provided a further support of the dissociable brain circuits for distinct phases of reward and avoidance learning. Also, the OCD and GD is characterised by aberrant patterns of reward and avoidance processing.

Second in a three-part lecture series about using matlab in neuroimaging. For full details: https://mailchi.mp/b3f65bbc346d/onneuro-7885485

First in a three-part lecture series about using matlab in neuroimaging. For full details go here: https://bit.ly/matlabimaging