We are looking for a researcher for an 18-month contract, as part of the project entitled "PaleoBRAIN: Resurrecting the brain of Homo erectus and Neandertals" funded by the ANR (ANR-20-CE27-0009). He/she will work in particular on the characterization of the form, the diversity and the evolution of the brain between neurosciences, neuroimaging and paleoanthropology. Missions: The main mission of the researcher will be to exploit a unique database in neuroimaging and paleoanthropology including complementary MRI data from the brains and skulls of volunteers and microtomographic data from human fossils. The objective is to characterize the link between the brain and the endocast (intracranial surface) in living humans to better understand the cerebral anatomy of fossils. Activities: Propose and develop, in collaboration with specialists in the different fields involved in the project, protocols for analyzing the link between brain and endocranium on the same series of volunteers (thanks to a set of MRI data obtained with complementary sequences). It will then be a question of applying this knowledge of the relation between the details of the cerebral surface and the internal surface of the skull to the study of fossil specimens. Finally, the researcher will contribute to the dissemination of data and communication around the project. The duration of this project is estimated at 18 months. The realization of the project is based on the finalization and the observation of the realization of the following points: validation of the protocol, acquisition of data on 60 individuals, realization of an "atlas" of the brain and the endocranium and a mapping of the link between the two models, but also the variability of the sulci (position, expression and variations on the two models), application to the fossil record to reconstruct a mean virtual brain of Homo erectus and H. neanderthalensis.

Research Fellow / Postdoc positions in Complex Networks and Brain Dynamics We are looking for new team members to join the Complex Networks and Brain Dynamics group to work on its interdisciplinary projects. The group is part of the Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences - based in Prague, Czech Republic, https://www.cs.cas.cz/. We focus on the development and application of methods of analysis and modelling of real-world complex networked systems, with particular interest in the structure and dynamics of human brain function. Our main research areas are neuroimaging data analysis (fMRI & EEG, iEEG, anatomical and diffusion MRI), brain dynamics modelling, causality and information flow inference, nonlinearity and nonstationarity, graph theory, machine learning and multivariate statistics; with applications in neuroscience, climate research, economics and general communication networks. More information about the group at http://cobra.cs.cas.cz/. Conditions: • Contract is for 6-24 months duration. • Positions are available immediately or upon agreement. • Applications will be reviewed on a rolling basis with a first cut-off point on 30. 09. 2022, until the positions are filled. • This is a full-time fixed term contract appointment. Part time contract negotiable. • Monthly gross salary: 42 000 – 55 000 CZK based on qualifications and experience. Cost Of Living Comparison • Bonuses and travel funding for conferences and research stays depending on performance. • No teaching duties.

A Postdoc or Junior Scientist position is available to join the Complex Networks and Brain Dynamics group for the project: “Predicting functional outcome in schizophrenia from multimodal neuroimaging and clinical data“ funded by the Czech Health Research Council. The project involves the development of tools to predict the functional outcome of schizophrenia from multimodal neuroimaging, clinical and cognitive measurements taken early after disease onset. To overcome limitations due to high dimensionality of data, we combine robust machine-learning tools, data-driven feature selection and theory-based brain network priors. The project is carried out in collaboration with the National Institute of Mental Health, using its unique large rich imaging, cognitive and biochemical data of early stage schizophrenia patients. Conditions: • Contract is of 12-30 months duration (with possibility of a follow-up tenure-track application). • Starting date: position is available immediately. • Applications will be reviewed on a rolling basis with a first cut-off point on 30. 9. 2022 • This is a full-time fixed term contract appointment. Part time contract negotiable. • Monthly gross salary: 42 000 – 48 000 CZK based on qualifications and experience. Cost Of Living Comparison • Bonuses depending on performance and travel funding for conferences and research stays. • Contribution for reallocation costs for succesful applicant coming from abroad: 10 000 CZK plus 10 000 CZK for family (spouse and/or children). • No teaching duties.

At Charité - Universitätsmedizin Berlin and the Bernstein Center for Computational Neuroscience, we are looking for a motivated and highly qualified PostDoc for methods development at the intersection of explainable machine learning / deep learning and clinical neuroimaging / translational psychiatry. The position will be located in the research groups of Ass. Prof. Kerstin Ritter and Prof. John-Dylan Haynes at Charité Berlin. The main task will be to predict response to cognitive-behavioral psychotherapy in retrospective data and a prospective cohort of patients with internalizing disorders including depression and anxiety from a complex, multimodal data set comprising tabular data as well as imaging data (e.g., clinical data, smartphone data, EEG, structural and functional MRI data). An additional task will be to contribute to the organization and maintenance of the prospective cohort. This study will be one of several projects in the newly established Research Unit 5187 "Precision Psychotherapy" (headed by Prof. Ulrike Lüken).

The Institute of General Psychology, Biopsychology and Methods of Psychology, Chair of Cognitive and Clinical Neuroscience (Prof. Katharina von Kriegstein, https://tu-dresden.de/mn/psychologie/ifap/kknw) offers, subject to the availability of resources, a position as Research Associate / PhD student / Postdoc (m/f/x) (subject to personal qualification employees are remunerated according to salary group E 13 TV-L) with 75% of the fulltime weekly working hours for doctoral candidates or 100% for postdocs. The position is starting as soon as possible and has a duration of 3 years with possible extension. The period of employment is governed by the Fixed Term Research Contracts Act (WissZeitVG). The position offers the chance to obtain further academic qualification (e.g. PhD / habilitation thesis). The project: The position is part of ReDyslexia funded by ERANET-NEURON (https://www.neuron-eranet.eu/projects/ReDyslexia/). ReDyslexia is a research consortium of neuroscientists and clinicians that have the aim (1) to better understand sensory pathway dysfunction in developmental dyslexia, and (2) to directly use this knowledge for improving treatment strategies. ReDyslexia includes studies in humans with developmental dyslexia as well as experiments in animal models. Tasks: The task involves (i) using a neuroimaging and behaviour database to assess sensory pathway dysfunction in dyslexia during childhood development, (ii) employing neurostimulation, neuroimaging, and behavioural measurements to assess and establish novel treatment approaches in adult dyslexics, and (iii) collaborating with clinicians and wet-lab neuroscientists to develop common experimental paradigms across different developmental stages and species. The setting: TU Dresden is one of eleven German Universities of Excellence. It provides an outstanding scientific infrastructure and ideal environment for interdisciplinary cooperation. Developmental neuroimaging projects will be conducted in collaboration with Dr. R. Bethlehem (University of Cambridge, https://www.autismresearchcentre.com/staff/richard-bethlehem/). Experiments will be performed at the Neuroimaging Centre (NIC, http://www.nic-tud.de). The NIC is equipped with a research-only MRI machine (Siemens 3T Prisma), MRI-compatible EEG, eye-tracking and noise-cancellation headphones, and a neurostimulation unit including TMS, tDCS, and tFUS. All experimental facilities are supported by experienced physics and IT staff. For computational work, there is access to the Centre for Information Services and High Performance Computing at TU Dresden. The TU Dresden Graduate Academy offers a comprehensive training programme and individual career advice for early career researchers (https://tu-dresden.de/ga?set_language=en). Applications from women are particularly welcome. The same applies to people with disabilities. Contact for Questions: For questions about the position please contact Prof. Dr. Katharina von Kriegstein (katharina.von_kriegstein@tu-dresden.de). Application instruction: Please submit your complete application including (a cover letter that briefly describes your personal qualifications and future research interests, CV, contact details of 2 personal references, and 1-2 publications as PDF for postdocs) by sending it as a single PDF document preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de (subject: ReDyslexia2022) to julia.herdin@tu-dresden.de or by mail to: TU Dresden, Fakultät Psychologie, Institut für Allgemeine Psychologie, Biopsychologie und Methoden der Psychologie, Professur für Kognitive und Klinische Neurowissenschaft, Prof. Katharina von Kriegstein, Helmholtzstr. 10, 01069 Dresden. The deadline for applications is February 23, 2022 (stamped arrival date of the university central mail service applies). Please submit copies only, as your application will not be returned to you.

** FULL JOB AD => please follow the link ** - https://tud.link/rm7g The project: The position is part of ReDyslexia funded by ERANET-NEURON (https://www.neuron-eranet.eu/projects/ReDyslexia/). ReDyslexia is a research consortium of neuroscientists and clinicians that have the aim (1) to better understand sensory pathway dysfunction in developmental dyslexia, and (2) to directly use this knowledge for improving treatment strategies. ReDyslexia includes studies in humans with developmental dyslexia as well as experiments in animal models. Tasks: The task involves (i) using a neuroimaging and behaviour database to assess sensory pathway dysfunction in dyslexia during childhood development, (ii) employing neurostimulation, neuroimaging, and behavioural measurements to assess and establish novel treatment approaches in adult dyslexics, and (iii) collaborating with clinicians and wet-lab neuroscientists to develop common experimental paradigms across different developmental stages and species The setting: TU Dresden is one of eleven German Universities of Excellence. It provides an outstanding scientific infrastructure and ideal environment for interdisciplinary cooperation. Developmental neuroimaging projects will be conducted in collaboration with Dr. R. Bethlehem (University of Cambridge, https://www.autismresearchcentre.com/staff/richard-bethlehem/). Experiments will be performed at the Neuroimaging Centre (NIC, http://www.nic-tud.de). The NIC is equipped with a research-only MRI machine (Siemens 3T Prisma), MRI-compatible EEG, eye-tracking and noise-cancellation headphones, and a neurostimulation unit including TMS, tDCS, and tFUS. All experimental facilities are supported by experienced physics and IT staff. For computational work, there is access to the Centre for Information Services and High Performance Computing at TU Dresden. The TU Dresden Graduate Academy offers a comprehensive training programme and individual career advice for early career researchers (https://tu-dresden.de/ga?set_language=en). Applications from women are particularly welcome. The same applies to people with disabilities. Contact for Questions: For questions about the position please contact Prof. Dr. Katharina von Kriegstein (katharina.von_kriegstein@tu-dresden.de). Application instruction: Please submit your complete application including (a cover letter that briefly describes your personal qualifications and future research interests, CV, contact details of 2 personal references, and 1-2 publications as PDF for postdocs) by sending it as a single PDF document preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de (subject: ReDyslexia2022) to julia.herdin@tu-dresden.de or by mail to: TU Dresden, Fakultät Psychologie, Institut für Allgemeine Psychologie, Biopsychologie und Methoden der Psychologie, Professur für Kognitive und Klinische Neurowissenschaft, Prof. Katharina von Kriegstein, Helmholtzstr. 10, 01069 Dresden. The deadline for applications is February 23, 2022 (stamped arrival date of the university central mail service applies). Please submit copies only, as your application will not be returned to you.

Few projects we will be pursuing include but not limited to: 1) Development of multimodal imaging biomarkers, targeting high-impact applications from early detection of Alzheimer’s disease and differential diagnosis to predicting response to treatment in Major Depression, 2) Development of standards for biomarker performance evaluation, 3) Quality control protocols for neuroimaging data (niQC), 4) Development of various other techniques and tools to achieve the above objectives, including multi-site data harmonization, conquering confounds in predictive analysis, advancing machine learning techniques for multimodal analysis (such as kernel methods, graph kernels and multiple kernel learning) and many others.

The lab of Dr. Kendrick Kay at the Center for Magnetic Resonance Research at the University of Minnesota is recruiting one or more postdocs. The lab seeks to integrate broad interdisciplinary insights to understand function in the visual system. One postdoc position is on a newly funded NIH R01 to develop, design, and collect a large-scale 7T fMRI dataset that samples a wide range of cognitive tasks on a common set of visual stimuli. The project is being conducted in close collaboration with co-PI Dr. Clayton Curtis (New York University). Activities in this grant include either (i) designing, collecting, and analyzing the large-scale neuroimaging dataset, (ii) technical work focused on extending and expanding the GLMsingle analysis method, and/or (iii) other related experimental or modeling work in visual/cognitive neuroscience. Another postdoc position is aimed towards integrating fMRI and intracranial EEG measurements during visual tasks (NSD-iEEG) and electrical stimulation. The general goal of this effort is to better understand signaling across the visual hierarchy (from early visual to higher order areas ventral temporal cortex and frontal/parietal areas). This project is in collaboration with PI Dr. Dora Hermes (Mayo Clinic).

The Diedrichsen Lab is looking to recruit a new postdoctoral associate with interest in studying the human cerebellum. The successful candidate will join an inter-disciplinary research group that uses behavioral, neuroimaging, and computational approaches to investigate the role of the cerebellum in motor control, cognition, and language. Our approach is to use Big Data and Machine Learning to gain insight about the overall functional organization of the cerebellum, which then informs targeted imaging and behavioural experiments. The enthusiastic and supportive environment will enable the candidate to develop their own research program.

Dr. Lei Zhang is looking for 2x PhD students interested in the cognitive, computational, and neural basis of (social) learning and decision-making in health and disease. The newly opened ALP(E)N Lab (Adaptive Learning Psychology and Neuroscience Lab) addresses the fundamental question of the “adaptive brain” by studying the cognitive, computational, and neurobiological basis of (social) learning and decision-making in healthy individuals (across the lifespan), and in psychiatric disorders. The lab combines an array of approaches including neuroimaging, patient studies and computational modelling (particularly hierarchical Bayesian modelling) with behavioural paradigms inspired by learning theories. The lab is based at the Centre for Human Brain Health and Institute of Mental Health at the University of Birmingham, UK, with access to exceptional facilities including MRI, MEG, TMS, and fNIRS. Funding is available through two competitive schemes from the BBSRC and MRC that provide a stipend, fees (at UK rate) and a research allowance, amongst other benefits. International (ie, outside UK) applicants are welcome.

The PhD project is focused on identifying the role of conscious perception through a neuroimaging and computational investigation. The project aims to characterise the neural correlates that support the formation of episodic memories and explain these findings with the Simultaneous Type/ Serial Token (STST) model, a neural network model of temporal attention. The project is competition-funded under the Midlands Integrative Biosciences Training Partnership, supported by the UK Biotechnology and Biological Sciences Research Council.

This Master’s is centered on research and on preparing students for a PhD in Psychology. Most of its core courses focus on hands-on in-lab research, science management and communication, and statistics, while offering the possibility of having many elective courses in computational biology/neuroscience, neuroimaging and others. The Master’s in Psychological Sciences has an English-only program available for those interested (the official languages are Portuguese and English). A major concentration of this Master’s will be in Cognitive Neuroscience, and will be associated with lab work and mentoring at the Proaction Lab lead by Jorge Almeida and within the transformative ERA Chair grant from the European Union to FPCE-UC CogBooster (lead by Alfonso Caramazza).

Bilkent University invites applications for multiple open-rank faculty positions in the Department of Neuroscience. The department plans to expand research activities in certain focus areas and accordingly seeks applications from promising or established scholars who have worked in the following or related fields: Cellular/molecular/developmental neuroscience with a strong emphasis on research involving animal models. Systems/cognitive/computational neuroscience with a strong emphasis on research involving emerging data-driven approaches, including artificial intelligence, robotics, brain-machine interfaces, virtual reality, computational imaging, and theoretical modeling. Candidates with a research focus in those areas whose research has a neuroimaging component are particularly encouraged to apply. The Department’s interdisciplinary Graduate Program in Neuroscience that offers Master's and PhD degrees was established in 2014. The department is affiliated with Bilkent’s Aysel Sabuncu Brain Research Center (ASBAM) and the National Magnetic Resonance Research Center (UMRAM). Faculty affiliated with the department has the privilege to access state-of-the-art research facilities in these centers, including animal facilities, cellular/molecular laboratory infrastructure, psychophysics laboratories, eyetracking laboratories, EEG laboratories, a human-robot interaction laboratory, and two MRI scanners (3T and 1.5T).

The DEB Lab is seeking to hire highly motivated postdocs or research associates with an interest in systems neuroscience and experience in in vivo electrophysiology. The DEB Lab combines cutting-edge experimental approaches in non-human primates, including simultaneous neuroimaging, neuro-electrophysiology, and body physiology. The aim of the lab is to examine the structural pathways and functional mechanisms underlying the role of interoception in neural network dynamics as well as in behavioral and physiological correlates of subjective perceptual awareness.

A 4-year fully-funded PhD studentship with Prof Mark Humphries and Dr JeYoung Yung at the University Of Nottingham is available to start September 2024. The project is titled 'Optimising patient selection for Deep Brain Stimulation in Parkinson’s disease using multimodal machine learning'. The goal of this project is to test how fusing clinical data, neuroimaging, and video assessments could optimise the selection of patients. The project will be in collaboration with MachineMedicine (London), a MedTech company specialising in Parkinson’s disease, and the movement disorders clinical team at St George’s Hospital, London. The PhD student will be trained in data-science and machine learning tools, including how to extract and analyse MRI and fMRI data, in fusing data across modalities, and in developing a machine-learning pipeline for predicting patient outcomes. These predictions will be tested against the 12-month follow-up data from the St George’s trial patients. The student’s further training will include a 3-month placement at MachineMedicine, and visits to St George’s clinic.

The PhD research topic will focus on understanding key mechanisms that enable specific cognitive functions in the brain, such as language comprehension, using a combination of computational neuroscience, machine learning, and experimental cognitive neuroscience techniques. The student will develop novel integrations of mechanistic physiological and generative AI-based theories of brain organization, and test these by designing, conducting, and analyzing experiments using advanced neuroimaging and neurostimulation technologies (EEG, fNIRS, TMS, MEG, fMRI, including mobile w/ VR/AR integration).

Over the last 20 years, neuroimaging and electrophysiology techniques have become central to understanding the mechanisms that accompany loss and recovery of consciousness. Much of this research is performed in the context of healthy individuals with neurotypical brain dynamics. Yet, a true understanding of how consciousness emerges from the joint action of neurons has to account for how severely pathological brains, often showing phenotypes typical of unconsciousness, can nonetheless generate a subjective viewpoint. In this presentation, I will start from the context of Disorders of Consciousness and will discuss recent work aimed at finding generalizable signatures of consciousness that are reliable across a spectrum of brain electrophysiological phenotypes focusing in particular on the notion of edge-of-chaos criticality.

Visual restoration after decades of blindness is now becoming possible by means of retinal and cortical prostheses, as well as emerging stem cell and gene therapeutic approaches. After restoring visual perception, however, a key question remains. Are there optimal means and methods for retraining the visual cortex to process visual inputs, and for learning or relearning to “see”? Up to this point, it has been largely assumed that if the sensory loss is visual, then the rehabilitation focus should also be primarily visual. However, the other senses play a key role in visual rehabilitation due to the plastic repurposing of visual cortex during blindness by audition and somatosensation, and also to the reintegration of restored vision with the other senses. I will present multisensory neuroimaging results, cortical thickness changes, as well as behavioral outcomes for patients with Retinitis Pigmentosa (RP), which causes blindness by destroying photoreceptors in the retina. These patients have had their vision partially restored by the implantation of a retinal prosthesis, which electrically stimulates still viable retinal ganglion cells in the eye. Our multisensory and structural neuroimaging and behavioral results suggest a new, holistic concept of visual rehabilitation that leverages rather than neglects audition, somatosensation, and other sensory modalities.

Efficient cognition requires flexible interactions between distributed neural networks in the human brain. These networks adapt to challenges by flexibly recruiting different regions and connections. In this talk, I will discuss how we study functional network plasticity and reorganization with combined neurostimulation and neuroimaging across the adult life span. I will argue that short-term plasticity enables flexible adaptation to challenges, via functional reorganization. My key hypothesis is that disruption of higher-level cognitive functions such as language can be compensated for by the recruitment of domain-general networks in our brain. Examples from healthy young brains illustrate how neurostimulation can be used to temporarily interfere with efficient processing, probing short-term network plasticity at the systems level. Examples from people with dyslexia help to better understand network disorders in the language domain and outline the potential of facilitatory neurostimulation for treatment. I will also discuss examples from aging brains where plasticity helps to compensate for loss of function. Finally, examples from lesioned brains after stroke provide insight into the brain’s potential for long-term reorganization and recovery of function. Collectively, these results challenge the view of a modular organization of the human brain and argue for a flexible redistribution of function via systems plasticity.

A discussion on some recent perspectives on pre-registration, which has become a growing trend in the past few years. This is not just limited to neuroimaging, and it applies to most scientific fields. We will start with this overview editorial by Simmons et al. (2021): https://faculty.wharton.upenn.edu/wp-content/uploads/2016/11/34-Simmons-Nelson-Simonsohn-2021a.pdf, and also talk about a more critical perspective by Pham & Oh (2021): https://www.researchgate.net/profile/Michel-Pham/publication/349545600_Preregistration_Is_Neither_Sufficient_nor_Necessary_for_Good_Science/links/60fb311e2bf3553b29096aa7/Preregistration-Is-Neither-Sufficient-nor-Necessary-for-Good-Science.pdf. I would like us to discuss the pros and cons of pre-registration, and if we have time, I may do a demonstration of how to perform a pre-registration through the Open Science Framework.

About 30% of children with cerebral palsy have congenital hemiplegia, resulting from periventricular white matter injury, which impairs the use of one hand and disrupts bimanual co-ordination. Congenital hemiplegia has a profound effect on each child's life and, thus, is of great importance to the public health. Changes in brain organization (neuroplasticity) often occur following periventricular white matter injury. These changes vary widely depending on the timing, location, and extent of the injury, as well as the functional system involved. Currently, we have limited knowledge of neuroplasticity in children with congenital hemiplegia. As a result, we provide rehabilitation treatment to these children almost blindly based exclusively on behavioral data. In this talk, I will present recent research evidence of my team on understanding neuroplasticity in children with congenital hemiplegia by using a multimodal neuroimaging approach that combines data from structural and functional neuroimaging methods. I will further present preliminary data regarding functional improvements of upper extremities motor and sensory functions as a result of rehabilitation with a robotic system that involves active participation of the child in a video-game setup. Our research is essential for the development of novel or improved neurological rehabilitation strategies for children with congenital hemiplegia.

Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory system, we show how activity is transformed within brain regions to yield characteristics specific to distinct output projections. We also show how this approach enables regional activity to be modeled in terms of inputs, in a paradigm that we are extending to address circuit-level origins of functional specialization in marmoset brains. In the second part of the talk, we will discuss how another genetic tool for MRI enables systematic studies of the relationship between anatomical and functional connectivity in the mouse brain. We show that variations in physical and functional connectivity can be dissociated both across individual subjects and over experience. We also use the tool to examine brain-wide relationships between plasticity and activity during an opioid treatment. This work demonstrates the possibility of studying diverse brain-wide processing phenomena using molecular neuroimaging.

Humans and other animals approach reward and avoid punishment and pay attention to cues predicting these events. Such motivated behavior thus appears to be guided by value, which directs behavior towards or away from positively or negatively valenced outcomes. Moreover, it is facilitated by (top-down) salience, which enhances attention to behaviorally relevant learned cues predicting the occurrence of valenced outcomes. Using human neuroimaging, we recently separated value (ventral striatum, posterior ventromedial prefrontal cortex) from salience (anterior ventromedial cortex, occipital cortex) in the domain of liquid reward and punishment. Moreover, we investigated potential drivers of learned salience: the probability and uncertainty with which valenced and non-valenced outcomes occur. We find that the brain dissociates valenced from non-valenced probability and uncertainty, which indicates that reinforcement matters for the brain, in addition to information provided by probability and uncertainty alone, regardless of valence. Finally, we assessed learning signals (unsigned prediction errors) that may underpin the acquisition of salience. Particularly the insula appears to be central for this function, encoding a subjective salience prediction error, similarly at the time of positively and negatively valenced outcomes. However, it appears to employ domain-specific time constants, leading to stronger salience signals in the aversive than the appetitive domain at the time of cues. These findings explain why previous research associated the insula with both valence-independent salience processing and with preferential encoding of the aversive domain. More generally, the distinction of value and salience appears to provide a useful framework for capturing the neural basis of motivated behavior.

There are over 30 million people with drug-resistant epilepsy worldwide. When neuroimaging and non-invasive neural recordings fail to localise seizure onset zones (SOZ), intracranial recordings become the best chance for localisation and seizure-freedom in those patients. However, intracranial neural activities remain hard to visually discriminate across recording channels, which limits the success of intracranial visual investigations. In this presentation, I present methods which quantify intracranial neural time series and combine them with explainable machine learning algorithms to localise the SOZ in the epileptic brain. I present the potentials and limitations of our methods in the localisation of SOZ in epilepsy providing insights for future research in this area.

The Open Source Imaging Initiative has recently released a fully open source low field MRI scanner called the OSI2ONE. We are currently building this system at the Universidad Paraguayo Alemana in Asuncion, Paraguay for a neuroimaging project at a clinic in Bolivia. I will discuss the process of construction, important considerations before you build, and future work planned with this device.

Dr. Nicholas Blauch will present on his work "Topoformer: Brain-like topographic organization in transformer language models through spatial querying and reweighting". Dr. Blauch is a postdoctoral fellow in the Harvard Vision Lab advised by Talia Konkle and George Alvarez. Paper link: https://openreview.net/pdf?id=3pLMzgoZSA Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Humans share with other animals a complex neuronal machinery that evolved to support navigation in the physical space and that supports wayfinding and path integration. In my talk I will present a series of recent neuroimaging studies in humans performed in my Lab aimed at investigating the idea that this same neural navigation system (the “brain GPS”) is also used to organize and navigate concepts and memories, and that abstract and spatial representations rely on a common neural fabric. I will argue that this might represent a novel example of “cortical recycling”, where the neuronal machinery that primarily evolved, in lower level animals, to represent relationships between spatial locations and navigate space, in humans are reused to encode relationships between concepts in an internal abstract representational space of meaning.

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

Lead author Mehdi Azabou will present on his work "POYO-1: A Unified, Scalable Framework for Neural Population Decoding" (https://poyo-brain.github.io/). Mehdi is an ML PhD student at Georgia Tech advised by Dr. Eva Dyer. Paper link: https://arxiv.org/abs/2310.16046 Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: Brain-optimized inference improves reconstructions of fMRI brain activity Abstract: The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively refine these reconstructions using a brain-optimized encoding model that maps images to brain activity. At each iteration, we sample a small library of images from an image distribution (a diffusion model) conditioned on a seed reconstruction from the previous iteration. We select those that best approximate the measured brain activity when passed through our encoding model, and use these images for structural guidance during the generation of the small library in the next iteration. We reduce the stochasticity of the image distribution at each iteration, and stop when a criterion on the "width" of the image distribution is met. We show that when this process is applied to recent decoding methods, it outperforms the base decoding method as measured by human raters, a variety of image feature metrics, and alignment to brain activity. These results demonstrate that reconstruction quality can be significantly improved by explicitly aligning decoding distributions to brain activity distributions, even when the seed reconstruction is output from a state-of-the-art decoding algorithm. Interestingly, the rate of refinement varies systematically across visual cortex, with earlier visual areas generally converging more slowly and preferring narrower image distributions, relative to higher-level brain areas. Brain-optimized inference thus offers a succinct and novel method for improving reconstructions and exploring the diversity of representations across visual brain areas. Speaker: Reese Kneeland is a Ph.D. student at the University of Minnesota working in the Naselaris lab. Paper link: https://arxiv.org/abs/2312.07705

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution (≈0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution (≈5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that MEG signals primarily contain high-level visual features, whereas the same approach applied to 7T fMRI also recovers low-level features. Overall, these results provide an important step towards the decoding - in real time - of the visual processes continuously unfolding within the human brain. Speaker: Dr. Paul Scotti (Stability AI, MedARC) Paper link: https://arxiv.org/abs/2310.19812

With the advent of several different fMRI analysis tools and packages outside of the established ones (i.e., SPM, AFNI, and FSL), today's researcher may wonder what the best practices are for fMRI analysis. This talk will discuss some of the recent trends in neuroimaging, including design optimization and power analysis, standardized analysis pipelines such as fMRIPrep, and an overview of current recommendations for how to present neuroimaging results. Along the way we will discuss the balance between Type I and Type II errors with different correction mechanisms (e.g., Threshold-Free Cluster Enhancement and Equitable Thresholding and Clustering), as well as considerations for working with large open-access databases.

Sound waves can be used to modify brain activity safely and transiently with unprecedented precision even deep in the brain - unlike traditional brain stimulation methods. In a series of studies in humans and non-human primates, I will show that Transcranial Ultrasound Stimulation (TUS) can have medium- to long-lasting effects. Multiple read-outs allow us to conclude that TUS can perturb neuronal tissues up to 2h after intervention, including changes in local and distributed brain network configurations, behavioural changes, task-related neuronal changes and chemical changes in the sonicated focal volume. Combined with multiple neuroimaging techniques (resting state functional Magnetic Resonance Imaging [rsfMRI], Spectroscopy [MRS] and task-related fMRI changes), this talk will focus on recent human TUS studies.

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

Advanced noninvasive neuroimaging methods provide valuable information on the brain function, but they have obvious pros and cons in terms of temporal and spatial resolution. Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) effect provides good spatial resolution in the order of millimeters, but has a poor temporal resolution in the order of seconds due to slow hemodynamic responses to neuronal activation, providing indirect information on neuronal activity. In contrast, electroencephalography (EEG) and magnetoencephalography (MEG) provide excellent temporal resolution in the millisecond range, but spatial information is limited to centimeter scales. Therefore, there has been a longstanding demand for noninvasive brain imaging methods capable of detecting neuronal activity at both high temporal and spatial resolution. In this talk, I will introduce a novel approach that enables Direct Imaging of Neuronal Activity (DIANA) using MRI that can dynamically image neuronal spiking activity in milliseconds precision, achieved by data acquisition scheme of rapid 2D line scan synchronized with periodically applied functional stimuli. DIANA was demonstrated through in vivo mouse brain imaging on a 9.4T animal scanner during electrical whisker-pad stimulation. DIANA with milliseconds temporal resolution had high correlations with neuronal spike activities, which could also be applied in capturing the sequential propagation of neuronal activity along the thalamocortical pathway of brain networks. In terms of the contrast mechanism, DIANA was almost unaffected by hemodynamic responses, but was subject to changes in membrane potential-associated tissue relaxation times such as T2 relaxation time. DIANA is expected to break new ground in brain science by providing an in-depth understanding of the hierarchical functional organization of the brain, including the spatiotemporal dynamics of neural networks.

Over the last decade, research on curiosity – the desire to seek new information – has been rapidly growing. Several studies have shown that curiosity elicits activity within the dopaminergic circuit and thereby enhances hippocampus-dependent learning. However, given this new field of research, we do not have a good understanding yet of (i) how curiosity-based learning changes across the lifespan, (ii) why some people show better learning improvements due to curiosity than others, and (iii) whether lab-based research on curiosity translates to how curiosity affects information seeking in real life. In this talk, I will present a series of behavioural and neuroimaging studies that address these three questions about curiosity. First, I will present findings on how curiosity and interest affect learning differently in childhood and adolescence. Second, I will show data on how inter-individual differences in the magnitude of curiosity-based learning depend on the strength of resting-state functional connectivity within the cortico-mesolimbic dopaminergic circuit. Third, I will present findings on how the level of resting-state functional connectivity within this circuit is also associated with the frequency of real-life information seeking (i.e., about Covid-19-related news). Together, our findings help to refine our recently proposed framework – the Prediction, Appraisal, Curiosity, and Exploration (PACE) framework – that attempts to integrate theoretical ideas on the neurocognitive mechanisms of how curiosity is elicited, and how curiosity enhances learning and information seeking. Furthermore, our findings highlight the importance of curiosity research to better understand how curiosity can be harnessed to improve learning and information seeking in real life.

Caroline Williams-Gray is a Principal Research Associate in the Department of Clinical Neurosciences, University of Cambridge, and an honorary consultant neurologist specializing in Parkinson’s disease and movement disorders. She leads a translational research group investigating the clinical and biological heterogeneity of PD, with the ultimate goal of developing more targeted therapies for different Parkinson’s subtypes. Her recent work has focused on the theory that the immune system plays a significant role in mediating the heterogeneity of PD and its progression. Her lab is investigating this using blood and CSF -based immune markers, PET neuroimaging and neuropathology in stratified PD cohorts; and she is leading the first randomized controlled trial repurposing a peripheral immunosuppressive drug (azathioprine) to slow the progression of PD.

This talk aims to highlight the immense potential of Artificial Intelligence (AI) in advancing the field of psychology and cognitive neuroscience. Through the integration of machine learning algorithms, big data analytics, and neuroimaging techniques, AI has the potential to revolutionize the way we study human cognition and brain characteristics. In this talk, I will highlight our latest scientific advancements in utilizing AI to gain deeper insights into variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. The presentation will showcase cutting-edge examples of AI-driven applications, such as deep learning for automated scoring of neuropsychological tests, natural language processing to characeterize semantic coherence of patients with psychosis, and other application to diagnose and treat psychiatric and neurological disorders. Furthermore, the talk will address the challenges and ethical considerations associated with using AI in psychological research, such as data privacy, bias, and interpretability. Finally, the talk will discuss future directions and opportunities for further advancements in this dynamic field.

Any sensory experience of the world, from the touch of a caress to the smile on our friend’s face, is embedded in time and it is often associated with the perception of the flow of it. The perception of time is therefore a peculiar sensory experience built without dedicated sensors. How the perception of time and the content of a sensory experience interact to give rise to this unique percept is unclear. A few empirical evidences show the existence of this interaction, for example the speed of a moving object or the number of items displayed on a computer screen can bias the perceived duration of those objects. However, to what extent the coding of time is embedded within the coding of the stimulus itself, is sustained by the activity of the same or distinct neural populations and subserved by similar or distinct neural mechanisms is far from clear. Addressing these puzzles represents a way to gain insight on the mechanism(s) through which the brain represents the passage of time. In my talk I will present behavioral and neuroimaging studies to show how concurrent changes of visual stimulus duration, speed, visual contrast and numerosity, shape and modulate brain’s and pupil’s responses and, in case of numerosity and time, influence the topographic organization of these features along the cortical visual hierarchy.

Sex hormones are powerful neuromodulators of learning and memory. In rodents and nonhuman primates estrogen and progesterone influence the central nervous system across a range of spatiotemporal scales. Yet, their influence on the structural and functional architecture of the human brain is largely unknown. Here, I highlight findings from a series of dense-sampling neuroimaging studies from my laboratory designed to probe the dynamic interplay between the nervous and endocrine systems. Individuals underwent brain imaging and venipuncture every 12-24 hours for 30 consecutive days. These procedures were carried out under freely cycling conditions and again under a pharmacological regimen that chronically suppresses sex hormone production. First, resting state fMRI evidence suggests that transient increases in estrogen drive robust increases in functional connectivity across the brain. Time-lagged methods from dynamical systems analysis further reveals that these transient changes in estrogen enhance within-network integration (i.e. global efficiency) in several large-scale brain networks, particularly Default Mode and Dorsal Attention Networks. Next, using high-resolution hippocampal subfield imaging, we found that intrinsic hormone fluctuations and exogenous hormone manipulations can rapidly and dynamically shape medial temporal lobe morphology. Together, these findings suggest that neuroendocrine factors influence the brain over short and protracted timescales.

It is increasingly recognized that epilepsy affects human brain organization across multiple scales, ranging from cellular alterations in specific regions towards macroscale network imbalances. My talk will overview an emerging paradigm that integrates cellular, neuroimaging, and network modelling approaches to faithful characterize the extent of structural and functional alterations in the common epilepsies. I will also discuss how multiscale framework can help to derive clinically useful biomarkers of dysfunction, and how these methods may guide surgical planning and prognostics.

Cortical variations in cytoarchitecture form a sensory-fugal axis that shapes regional profiles of extrinsic connectivity and is thought to guide signal propagation and integration across the cortical hierarchy. While neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy, indicating that bottom-up transitions were easier to complete compared to top-down. Supporting analyses demonstrated that asymmetries were underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Lastly, we found that asymmetries correlated with differences in communicability and intrinsic neuronal time scales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

The ability to build, store, and manipulate semantic representations lies at the core of all our (inter)actions. Combining evidence from cognitive neuroimaging and experimental neuropsychology, I study the neurocognitive correlates of semantic knowledge in relation to other cognitive functions, chiefly language. In this talk, I will start by reviewing neuroimaging findings supporting the idea that semantic representations are encoded in distributed yet specialized cortical areas (1), and rapidly recovered (2) according to the requirement of the task at hand (3). I will then focus on studies conducted in neurodegenerative patients, offering a unique window on the key role played by a structurally and functionally heterogeneous piece of cortex: the anterior temporal lobe (4,5). I will present pathological, neuroimaging, cognitive, and behavioral data illustrating how damages to language-related networks can affect or spare semantic knowledge as well as possible paths to functional compensation (6,7). Time permitting, we will discuss the neurocognitive dissociation between nouns and verbs (8) and how verb production is differentially impacted by specific language impairments (9).

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

We will explore how neuropsychology can be leveraged to directly test cognitive neuroscience theories using the case of frontotemporal dementias affecting the language network. Specifically, we will focus on pathological, neuroimaging, and cognitive data from primary progressive aphasia. We will see how they can help us investigate the reading network, semantic knowledge organisation, and grammatical categories processing. Time permitting, the end of the talk will cover the temporal dynamics of semantic dimensions recovery and the role played by the task.

Predictive modeling and dimensionality reduction of functional neuroimaging data have provided rich information about the representations and functional architectures of the human brain. While these approaches have been effective in many cases, we will discuss how neglecting the internal dynamics of the brain (e.g., spontaneous activity, global dynamics, effective connectivity) and its underlying computational principles may hinder our progress in understanding and modeling brain functions. By reexamining evidence from our previous and ongoing work, we will propose new hypotheses and directions for research that consider both internal dynamics and the computational principles that may govern brain processes.

It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

There has been increasing interest in the neurocomputational mechanisms underlying psychotic disorders in recent years. One promising approach is based on the theoretical framework of predictive processing, which proposes that inferences regarding the state of the world are made by combining prior beliefs with sensory signals. Delusions and hallucinations are the core symptoms of psychosis and often co-occur. Yet, different predictive-processing alterations have been proposed for these two symptom dimensions, according to which the relative weighting of prior beliefs in perceptual inference is decreased or increased, respectively. I will present recent behavioural, neuroimaging, and computational work that investigated perceptual decision-making under uncertainty and ambiguity to elucidate the changes in predictive processing that may give rise to psychotic experiences. Based on the empirical findings presented, I will provide a more nuanced predictive-processing account that suggests a common mechanism for delusions and hallucinations at low levels of the predictive-processing hierarchy, but still has the potential to reconcile apparently contradictory findings in the literature. This account may help to understand the heterogeneity of psychotic phenomenology and explain changes in symptomatology over time.