We are at a unique time in history, when global large-scale projects are generating an unprecedented amount of data. Although much of this data is "open" and available—with analysis tools developed by a new generation of neuroinformaticians—some is still just beyond the reach of many neuroscientists.

In this Short Course* from Neuroscience 2017, leaders in the neuroinformatics field highlight some of the most broadly accessible open datasets and help you embark on their independent scientific voyage of discovery.

*This course includes demonstrations and tutorials using technical computing software (R-Studio, Python, Matlab).

Speakers

Jane Roskams, PhD

Jane Roskams is a professor in the center for brain health at the University of British Columbia and in the department of neurosurgery at the University of Washington. Roskams was previously the executive director at The BRAIN Commons, chief scientific officer and co-founder at Cascadia Data Federation, and a professor of zoology at the University of British Columbia. Her research interests lie in the development and regeneration of the nervous system. Roskams earned her PhD in neuroscience from the Penn State College of Medicine and completed her postdoctoral training in neuroscience and neuropathology at Johns Hopkins School of Medicine.

Trygve Bakken, MD, PhD

Trygve Bakken is a scientist at the Allen Institute for Brain Science and is helping build a quantitative census of cell types in the human brain. He builds computational tools to characterize the transcriptomic diversity of cell types with the aim to understand cell type evolution and role in neuropsychiatric disease. He earned his BA from Yale University, MS in physics and philosophy from the London School of Economics, and PhD in neuroscience and MD from the University of California, San Diego.

Paul Pavlidis, PhD

Paul Pavlidis is a professor of psychiatry at the University of British Columbia, with joint appointments in the Michael Smith Laboratories and the Djavad Mowafaghian Centre for Brain Health. He previously worked as a faculty member in the department of biomedical informatics at Columbia University. His research focuses on computational and bioinformatic applications of genomics to neuroscience, including large-scale meta-analysis, gene networks, and cross-modality data integration. Pavlidis received his BA in biological sciences from Cornell University and his PhD in molecular and cell biology from the University of California, Berkeley. He completed his postdoctoral training at Stanford University and Columbia University.

Joshua Vogelstein, PhD

Joshua Vogelstein is an assistant professor in the department of biomedical engineering and the Institute for Computational Medicine at Johns Hopkins University. His work focuses largely on big and wide data, especially in statistics of brain graphs (connectomes). He cofounded the Open Connectome Project with R. Jacob Vogelstein and Randal Burns, an associate professor in the department of computer science at Johns Hopkins University.

Sean Hill, PhD

Sean Hill is the inaugural director of the Krembil Centre for Neuroinformatics at the Centre for Addiction and Mental Health. He previously served as the executive director and scientific director of the International Neuroinformatics Coordinating Facility at the Karlinska Institute in Stockholm, Sweden. Hill has extensive experience in building and simulating large-scale models of brain circuitry and has also supervised and led research efforts exploring the principles underlying the structure and dynamics of neocortical and thalamocortical microcircuitry. He received his PhD in computational neuroscience at the Université de Lausanne in Switzerland and completed his postdoctoral training at the Neurosciences Institute and at the University of Wisconsin.

Kenneth D. Harris, PhD

Kenneth Harris is a professor of quantitative neuroscience at University College London. He previously worked as an associate professor of neuroscience at Rutgers University. His research focuses on the computation and biology of neuronal self-organization through quantitative analysis of large-scale neuronal recordings, and theoretical models of neuronal computation. He studied mathematics at Cambridge University and received his PhD in robotics from University College London and completed his postdoctoral work at Rutgers University.

Marius Pachitariu, PhD

Marius Pachitariu is a group leader at the Janelia Research Campus at the Howard Hughes Medical Institute. His research focuses on understanding how the coordinated activity of large populations of neurons gives rise to perception and behavior and deciphering the cortical code. Pachitariu received his BS in mathematics from Princeton University and his PhD from University College London.

Nick Steinmetz, PhD

Nick Steinmetz is a senior research associate at University College London and the program coordinator for a collaborate grant from the Welcome Trust to develop new versions of neuropixels probes. His research focuses on understanding the neural circuits and systems that underlie perception and cognition across the brain. Steinmetz received his BS in bioengineering from the University of Pennsylvania and his PhD in neuroscience from Stanford University.

Jean-Baptiste Poline, PhD

Jean-Baptiste Poline is an associate professor of neuroinformatics at McGill University. Poline also serves as the chair of the neuroimaging data sharing task force of the International Neuroinformatics Coordinating Facility and the co-editor-in-chief of Frontiers in Brian Imaging Methods. He previously worked as a researcher at the Helen Wills Neuroscience Institute at the University of California, Berkeley. His research focuses on developing methods for resting-state fMRI and imaging genetic data using clustering techniques, teaching neuroimaging data analysis in Python, and working to better understand and propose solutions to the neuroimaging reproducibility issues. He received his MS and PhD in biomathematics from the University of Paris VII.

Katrin Amunts, MD, PhD

Katrin Amunts is a professor and of the C. and O. Vogt Institute for Brain Research, Heinrich-Heine University Duesseldorf and director of the Institute of Neuroscience and Medicine, Research Centre Juelich. Amunts is a member of the editorial board of Brain Structure and Function, and vice chair of the German Ethics Council. She is also the co-speaker of the graduate school Max-Planck School of Cognition. She studied medicine and biophysics at Pirogov Medical School in the former Soviet Union and received her PhD in neuroscience and anatomy from the Institute of Brain Research in Russia.

Jennifer Whitesell, PhD

Jennifer Whitesell is a scientist at the Allen Institute, where she works on an NIH-funded project mapping connectivity in mouse models of Alzheimer’s disease. Her interests include understanding neural circuit architecture and how neuronal connections support information coding. Whitesell received her BS and MS degrees in biochemistry from Colorado State University and her PhD in neuroscience from the University of Colorado Denver.

Material below summarizes the article Exploring Individual Brain Connectomes With Topological Data Analysis in ADHD, published on April 21, 2020, in eNeuro and authored by Zeus Gracia-Tabuenca, Juan Carlos Díaz-Patiño, Isaac Arelio, and Sarael Alcauter.

Highlights

• Topological Data Analysis (TDA) showed significant differences between ADHD and typically developing children.

• Such differences are not dependent on how we segment the brain into multiple regions.

• ADHD patients showed higher connectivity than controls. Particularly, two high-order or association networks showed the most increases in connectivity: the frontal lobe and the default mode network.

Figure from "Exploring Individual Brain Connectomes With Topological Data Analysis in ADHD," published on published on April 21, 2020, in eNeuro and authored by Zeus Gracia-Tabuenca, Juan Carlos Díaz-Patiño, Isaac Arelio, and Sarael Alcauter.

Study Question

Topological Data Analysis (TDA) is a mathematical framework that extracts high-dimensional information from complex systems. In this study, we asked, What insights can we obtain when modeling the brain network as a topological space (a set of connected points)? Are these insights robust when using different brain parcellations? And, can we differentiate controls from ADHD patients using TDA?

How This Research Advances What We Know

Topological Data Analysis (TDA) is a mathematical framework that extracts high-dimensional information from complex systems. In this study, we asked, What insights can we obtain when modeling the brain network as a topological space (a set of connected points)? Are these insights robust when using different brain parcellations? And, can we differentiate controls from ADHD patients using TDA?

How This Research Advances What We Know

The brain is a complex system of interacting elements at different anatomical and temporal scales. A common approach to explore such a system involves modeling the brain as a complex network or connectome, and using some metrics to capture brain organization properties, such as global integration or local segregation. For example, regarding ADHD, some studies have related this developmental disorder with a disruption of attentional systems that usually involve the communication between the anterior and posterior parts of the brain (fronto-parietal), while others have stressed global effects to the brain. However, the results of these studies vary depending on how the brain regions are defined, in other words, how the brain is segmented or divided into several regions. But the results may also change depending on how we define if two brain regions are synchronized or not. This is usually done by a simple metric, the correlation between their activity patterns, these being measured with functional MRI in this case. But still, we have to define a quantitative threshold for the correlation values to define if two brain regions are in synchrony or not. Different thresholds may yield variable results, complicating the interpretation of findings. Instead of selecting a single connectivity threshold, here we explored how the brain network changes as a function of the connectivity threshold (exploring the whole range of possible thresholds), and if this pattern of change is different between ADHD and controls. We also tested how robust the results are when using different brain segmentation schemes.

Experimental Design or Methodology

Resting-state fMRI scans from 96 controls and 81 ADHD patients (7-18 years old) were studied. From this, we can build a connectome for each session taking the pairwise synchrony (Pearson's cross-correlation) of the fMRI signal along multiple regions of the brain. Then we can measure how fast different regions of the brain bind together if we change the synchrony threshold; in human connectomes this transition has an inverse S-shape curve (top figure). In TDA terms, this is called the B0 curve. We test the robustness of the B0 curves using different brain segmentations (based on anatomy, function, and homogeneity). Can diagnostic groups be differentiated by the B0 curves? And if yes, where are these differences more patent?

Results

We found that the area-under-the-curve (AUC) of the TDA's B0 curves are highly consistent no matter how we divide the brain (if based on anatomy, function, or homogeneity). Regarding group inferences, ADHD group showed lower AUC compared to controls. That means that the ADHD connectome binds together faster than controls when changing the synchrony threshold, in other words, the connectomes of the ADHD patients are over synchronized compared to those of controls. In addition, this pattern was more prominent for the interactions of the frontal lobe and the default mode network, which is consistent with the hypothesis of altered attentional fronto-parietal systems.

Interpretation

Our results showed that the connectomes of ADHD patients have stronger connections than those of the controls. We also showed that these differences between diagnostic groups are robust, and not dependent on how we segment the brain. Moreover, we identified that the main differences between ADHD and controls involve the interactions of the frontal lobe and the default mode network, important brain systems whose alterations have been related to attention deficits. Our results seem consistent with the hypothesis of the alteration of fronto-parietal attentional systems.

TDA is a robust methodology that complements previous studies on the brain functional connectome. All computing code applied in this work is freely available (Extended Data 1, in the original publication), hence this approach can be further applied to other brain networks such as different species or different clinical populations.

Visit eNeuro to read the original article and explore other content. Read other summaries of eNeuro and JNeurosci papers in the Neuronline collection SfN Journals: Research Article Summaries.

Exploring Individual Brain Connectomes With Topological Data Analysis in ADHD. Zeus Gracia-Tabuenca, Juan Carlos Díaz-Patiño, Isaac Arelio, and Sarael Alcauter. eNeuro  21 April 2020, 7 (3) 0543-19.2020; DOI: 10.1523/ENEURO.0543-19.2020

About the Contributors

Zeus Gracia-Tabuenca

Zeus Gracia-Tabuenca is a PhD candidate in biomedical sciences, at the National Autonomous University of Mexico (UNAM).

Juan Carlos Díaz-Patiño, PhD

Juan Carlos Díaz-Patiño holds a PhD in mathematics from the Universidad National Autonomous University of Mexico (UNAM).

Isaac Arelio, PhD

Isaac Arelio holds a PhD in mathematics from the Universidad National Autonomous University of Mexico (UNAM).

Sarael Alcauter, PhD

Sarael Alcauter is an assistant professor in the department of behavioral and cognitive neurobiology at the Institute of Neurobiology, National Autonomous University of Mexico (UNAM).

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