Date: January 19th, 2024, Time: 15:00 CET, 9:00 EST
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Abstract: In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. In this talk, I position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. I argue that the neuroscientific community should take an integrative approach whereby MRI-based and electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Biosketch: Arianna Sala is currently a FNRS post-doctoral research fellow at the Coma Science Group, University of Liège, Belgium. After a PhD in molecular medicine investigating molecular biomarkers in Alzheimer’s disease, she joined the Coma Science Group to study brain glucose metabolism and metabolic networks in disorders of consciousness. She is co-founder of the Molecular Connectivity Working Group, a multidisciplinary initiative that aims to evaluate molecular imaging for the study of brain connectivity.

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Date: February 23rd, 2024, Time: 15:00 CET, 9:00 EST
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Abstract: I will (1) go through some of the basics of PCA with toy simulations, (2) discuss some of the recent criticisms of PCA, and (3) show examples in fMRI data to demonstrate the power of multivariate compared to univariate analysis. I hope to convince the audience that an intelligent use of PCA is the best go-to initial pass at complex data, and also provides a good benchmark for more complicated and deeper learning architectures.

Biosketch: Chris Habeck was originally trained as a Particle Physicist, but followed the great migration from Physics to the lifesciences after his PhD. After a brief stint of biophysical computational modeling at the Neurosciences Institute in San Diego, he came to brain imaging analytics with PET and fMRI at Columbia University where he has been ever since. Chris has conducted, developed, and popularized multivariate analysis frameworks and non-parametric statistics, relying on the trusty workhorse of PCA: “My hope is that every practitioner -from seasoned analyst to novice- will get something out of my talk.“

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Date: March 22nd, 2024, Time: 15:00 CET, 10:00 EST

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Abstract: Anatomy based atlases are often used to summarize positron emission tomography (PET) data. However, it is well known that functional boundaries do not correspond well to anatomic boundaries. In addition, anatomic atlases do not capture variation among individuals and may average together voxels which are functionally distinct. In contrast, independent component analysis (ICA) provides informative data-driven output which also allows for overlap across anatomic regions. However, the output of blind ICA, without any a priori information, can be challenging to compare across studies and requires manual selection of components of interest. Here we propose the use of a spatially constrained ICA approach, called NeuroMark PET, leveraging a priori PET template derived from ICAs which replicated across independent PET datasets. We use this to generate a radioligand specific template which is then used to automatically generate component maps (i.e., covarying networks) and subject specific output using spatially constrained ICA. We also show that the ICA component maps are invariant to standard update value ratio (SUVR) scaling, allowing easy rescaling of the component loadings as desired. The proposed Neuromark PET approach effectively captures biologically meaningful subject-specific features that are comparable across different individuals. We demonstrate by comparing group differences in PET networks as well as age-related changes. This approach also allows for comparison across modalities or radioligands. We show an example of this by using a widely used functional MRI NeuroMark template as spatial priors for PET data, revealing PET specific modularity in fMRI intrinsic networks. In sum, we describe a PET NeuroMark template and pipeline and suggest that it represents a powerful resource for the research community and for analyses with large multimodal datasets.

Biosketch: Dr. Calhoun is founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) where he holds appointments at Georgia State, Georgia Tech and Emory. He is the author of more than 1000 full journal articles. His work includes the development of flexible methods to analyze neuroimaging data including blind source separation, deep learning, multimodal fusion and genomics, neuroinformatics tools. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The American Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, The Organization for Human Brain Mapping (OHBM) and the International Society of Magnetic Resonance in Medicine. He currently serves on the IEEE BISP Technical Committee and is also a member of IEEE Data Science Initiative Steering Committee as well as the IEEE Brain Technical Committee.

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Date: April 19th, 2024 Time: 15:00 CET, 9:00 EST

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Regional segregation and long-range integration are the two fundamental principles of brain organization. As connectivity patterns play a major role in both of these, assessing the the connectome has emerged as a key avenue towards a better understanding of the brain. Key to this presentation is the notion that there is no such thing as “the” connectivity between two brain regions, but rather different concepts, facets and assessment of brain connectivity may provide corroborating, complementary or conflicting evidence. Therefore, a better understanding of the relationship between connectivity-types and their integration to a more holistic view of long-range interactions should be critical to avoid an undue reliance on individual approaches that may actually hinder progress in neurobiological understanding.

Simon Eickhoff is a full professor and chair of the Institute for Systems Neuroscience at the Heinrich-Heine University in Düsseldorf and the director of the Institute of Neuroscience and Medicine (INM-7, Brain and Behavior) at the Forschungszentrum Jülich. He is furthermore a visiting professor at the Chinese Academy of Science Institute of Automation. Working at the interface between neuroanatomy, data-science and brain medicine, he aims to obtain a more detailed characterization of the organization of the human brain and its inter-individual variability in order to better understand its changes in advanced age as well as neurological and psychiatric disorders. This goal is pursued by the development and application of novel analysis tools and approaches for large-scale, multi-modal analysis of brain structure, function and connectivity as well as by machine-learning for single subject prediction of cognitive and socio-affective traits and ultimately precision medicine.

Date: May 17th, 2024 Time: 15:00 CET, 9:00 EST

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Identifying unique differences is crucial for enhancing personalized medicine strategies for Alzheimer’s disease (AD). In this work, we present a framework to create individual molecular connectomes by using longitudinal data from tau and amyloid positron emission tomography scans. We show that these personalized molecular connectomes can pinpoint specific individuals and track disease progression throughout different AD stages as well as over time.

I have a background in neuroimaging in aging and neurodegenerative diseases, mainly Alzheimer’s disease (AD). In my group we are working with different brain imaging modalities, CSF and plasma biomarkers to understand the sequence of pathological events that lead to AD. We have developed different methods, including an open-access software for the analysis of brain connectivity using graph theory, multilayer network approaches and deep learning called BRAPH. 

You can find more about Joana here.