Annual Meeting - Talairach Lecture
 
 

Dr. Andres Lozano, OC, MD, Ph.D, FRCSC, FRSC, FCAHS

Title: Imaging for the Discovery and Validation of Therapeutic Brain Targets in Functional Neurosurgery

Abstract: Advances in brain imaging have increased the understanding of brain circuits and opened exciting new therapeutic possibilities in Functional Neurosurgery. Among its attributes, brain imaging has identified novel therapeutic targets and has allowed the optimization of surgical therapies including deep brain stimulation (DBS) and focused ultrasound (FUS). Brain imaging has also reveled unanticipated observations which are providing new ways of thinking about the brain and brain therapies.


Annual Meeting - Keynote Speakers
 
   

Dr. Sarah Genon, Ph.D

Title: From the Complexity of Brain Organization to Challenges in Brain Behaviour Mapping

Abstract: Understanding brain-behavior relationships in humans remains as one of the most complex scientific questions. For a few decades, data offered by neuroimaging approaches, in particular MRI, have been under intense scientific investigations and methodological questioning. These have highlighted continuous challenges and open questions. Several principles and challenges in studying brain organization and brain-behavior relationships will be illustrated here by one of the most studied brain regions: The hippocampus. Beyond regional mapping, multivariate brain mapping to behaviour has more recently opened new perspectives by revealing complex patterns of brain-behaviour relationships. However, these approaches also come with their own challenges. In that framework, I will here point to two major questions tackled in our studies: Generalizability and Interpretability.

   

Ole Jensen, Ph.D

Title: An Oscillatory Pipeline Mechanism Supporting Fast Processing During Visual Exploration and Reading

Abstract: We saccade every ~250 ms during visual exploration and reading. Given that it takes ~100 ms to prepare and execute a saccade, the visual system has less than 150 ms to identify the fixated object (or word) while also partaking in preparing the next saccade goal. To achieve this fast processing, we propose that neuronal computations in the ventral stream are supported by a pipelining mechanism in which serial processing of several objects (or words) occurs at each level in the visual hierarchy while parallel processing occurs across these levels. We suggest that alpha oscillations serve to coordinate this processing as well as saccade initiations. We have investigated this framework by acquiring MEG and eye-tracking data from participants performing free viewing and reading tasks. In particular, a recent innovation - rapid invisible frequency tagging - as well as multivariate pattern analysis have helped us to provide support for the proposed pipelining mechanism.

   

Dr. Yina Ma, Ph.D

Title: Behavioral and Neurophysiological Mechanisms of Social Cooperation

Abstract: Human society operates on large-scale cooperation. However, individual differences in cooperation and incentives to free-riding make large-scale cooperation fragile and can lead to reduced social welfare. Deciphering the neural codes representing potential rewards/costs for self and others is crucial for understanding social cooperation. First, we integrated computational modeling with functional MRI to investigate neural representations of social value. Individually preferred social allocation serves as a reference-point for computing social value, and amygdala and lateral orbitofrontal activity encoded social-value distance signals. Second, we simultaneously recorded neural activity of 6 individuals involved in intergroup conflict using hyper-fNIRS and highlighted the role of within-group synchronized reduction in prefrontal activity in intragroup cooperation and intergroup hostility. Third, we employed psychological manipulations and pharmacological challenge of oxytocin to alter interpersonal, intergroup, and network cooperation. Finally, social cooperation requires not only each cooperation party to continuously track partner's intention/action but also the team to approach collective goals. We developed a novel coordination racing game and dissociated two essential cooperation components: Interpersonal coordination and collective goal pursuit. Using intracranial EEG recording of local field potentials, we showed that high-gamma oscillations in the temporoparietal junction encoded the primary motive of different cooperation stages. Moreover, the two cooperating brains synchronized their TPJ and amygdala high-gamma activity in a way that predicted their motives underlying successful cooperation. Taken together, I'd share my understanding of social cooperation from an interactive and dynamic view, and the necessity goes from, "how are cooperation decisions made" to "how to initiate and maintain cooperation."

   

Dr. Janaina Mourao-Miranda, Ph.D

Title: Machine Learning and Psychiatric Neuroimaging: Searching for the Hidden Patterns of Mental Health

Abstract: Over the past years machine learning and artificial intelligence have hugely impacted many areas, including marketing, finance and medicine, These techniques can find hidden patterns in high dimensional and complex data enabling predictions not previously possible. Among all medical areas, psychiatry can potentially be the one that will be most transformed by artificial intelligence and machine learning due to the current lack of biomarkers. Advanced machine learning models that can combine information from neuroimaging techniques with complementary knowledge from clinical assessments and general patient information have the potential to identify reliable biological markers and improved patient stratification. In this talk, I will present some pioneering examples of machine learning applications for diagnosis and prognosis of psychiatric disorders based on neuroimaging and discuss the challenges faced by these applications. I will also discuss how machine learning models can help us to identifying key axes of covariation between brain and psychosocial variables which can improve our understanding of mental health variability across the general population and bring new insights on prevention and treatment of mental health disorders.

   

Dr. Jonathan R. Polimeni, Ph.D

Title: How Far Can We Go with Functional MRI? What the Blood Vessels Can Tell Us.

Abstract: All fMRI techniques in use today measure brain function only indirectly, by tracking the changes in blood flow, volume, and oxygenation that accompany neuronal activity, and this has often been viewed as the fundamental limitation of the technique. However, recent evidence from microscopy studies has shown that the smallest blood vessels of the brain appear to respond far more precisely, in space and in time, to neuronal activity than previously believed. This insight suggests that the "biological resolution" of fMRI is intrinsically high, and, with sufficiently high imaging resolution, it should fundamentally be possible to extract more meaningful neuronally specific information from fMRI - provided that we understand how vascular anatomy and physiology shape the hemodynamics that generate the fMRI signals.

In this talk, I will describe ongoing efforts to improve the neuronal specificity of fMRI and pose the question: How far can we go with fMRI? The limits of spatial and temporal resolution of fMRI are actively being investigated using advanced imaging technologies. While high-resolution human fMRI studies are increasingly operating at the boundaries of what is achievable, a key challenge is that the vascular architecture of the brain reflects its structure and function across spatial scales. Both classic and modern vascular anatomy studies have shown how the macro-vascular geometry is couple to the tissue geometry, including the gray matter folds and the white matter tracts, while the micro-vascular density closely follows borders of subcortical nuclei, cortical areas and cortical layers. I will present evidence that both the large-and-small-scale vascular anatomy strongly influence patterns of fMRI activation and describe strategies for how to account for this.

As examples of the intrinsically high biological resolution of fMRI, I will present results showing cortical columnar and laminar imaging, and new directions in the emerging field of "fast fMRI" that show how the BOLD response can track surprisingly fast neural dynamics. Lastly, I will share our recent progress towards building bottom-up biophysical models of the fMRI signals based on realistic large-and small-scale vascular networks and dynamics that can potentially help solve the "inverse problem" of inferring neuronal activity. Overall, many lessons can be learned through a deeper understanding of vascular anatomy and physiology, which can both shed light on the functional organization of the brain and help neuroimagers more accurately interpret the fMRI signals in terms of the underlying neural activity.

   

Anastasia Yendiki, Ph.D

Title: Diffusion Tractography Under the Microscope: What Works, What Doesn't, What Next.

Abstract: Diffusion tractography, now in its third decade of being used to image white-matter pathways in vivo in the human brain, has gone through its fair share of both hype and controversy. In this talk, I will discuss the lessons learned from our experience developing tractography tools for in vivo studies, as well as comparing tractography to anatomy in ex vivo studies. What works? Diffusion MRI is remarkably good at reconstructing known anatomy. When the tractography algorithm is infused with anatomical knowledge, the large highways of the brain can be reconstructed accurately without the need for a sophisticated scan protocol. The latest advances in hardware and sequences for diffusion MRI allow us to go well beyond that, enabling the reconstruction of intricate topographies of small axon bundles within the large white-matter highways, heretofore only seen with invasive anatomic studies. What doesn't work? Diffusion tractography cannot be used in lieu of anatomic studies, i.e., it cannot be used as the sole source of evidence that two brain regions are not connected to each other. That is because, in its purely unsupervised form, tractography will produce not only anatomically correct solutions, but also erroneous ones. While it was established early on that tractography can go wrong, post mortem studies are now providing converging evidence on precisely where and why it goes wrong. What next? I will argue improvements in diffusion MRI data quality will not be sufficient to solve these errors of tractography, as they stem from a limitation of the current analytic paradigm. I will end by proposing a path forward that relies on ground truth axonal orientations from post mortem microscopy, not only to asses the accuracy of existing diffusion tractography algorithms, but to engineer the next generation of algorithms.

   

Dr. Andrew Zalesky, Ph.D

Title: Harnessing Individaul Variability of Human Brain (and body) Systems Across Health and Disease.

Abstract: Understanding the vast variability in brain structure and function has emerged as a major goal in neuroscience. This presentation will explore progress towards establishing normative reference charts to capture the extraordinary range of individual variability in brain (and body) systems across the human lifespan and in disease. I will demonstrate how we have harnessed individual variability to enhance brain-based therapeutics for psychiatric disorders and map multiorgan brain-body interactions. 

   

Dr. Juan Helen Zhou, Ph.D

Title: Differential Brain Network Phenotypes in Neurological Disorders: A Longitudinal Perspective

Abstract: Each neurodegenerative disease targets a distinct large-scale network in the human brain. Brain network-sensitive neuroimaging methods such as resting-state fMRI and diffusion MRI can shed light on brain network dysfunction trajectories in neurological disorders from the preclinical to the clinical stage. I will discuss the differential brain structural and functional network phenotypes related to dementia subtypes and pathology (e.g. amyloid and cerebrovascular). Evidence on how individual-level brain network integrity contributes to cognitive and behavior problems over time will be presented. I will highlight the challenges in studying selective brain network vulnerability across the disease spectrum and the potential of machine learning and computational approaches. Further developed, multimodal network-specific imaging signatures can help reveal disease mechanisms and facilitate early detections, prognosis, and treatment development of neurological disorders.

   

 

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