The document summarizes a seminar given by Ana Luisa Pinho on her work analyzing the Individual Brain Charting (IBC) dataset. The IBC dataset consists of high-resolution fMRI scans of 12 healthy adults performing a variety of cognitive tasks. Pinho discussed assessing the quality of data in the IBC First Release, functional mapping of individual brains using the dataset, and encoding analysis of natural stimuli using the Fast Shared Response Model on the IBC Third Release. The goal is to leverage the large, high-quality IBC dataset to better map the relationship between brain systems and mental functions.
Deep behavioral phenotyping in functional MRI for cognitive mapping of the hu...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required, by pooling data or results from different single-task studies. Meta-analyses allow the accumulation of knowledge across studies. Yet, they are typically impacted not only by inter-subject and inter-site variability but also loss of information from sparse peak-coordinate representations. In this talk, I will address a battery of studies, which combine deep phenotyping and multitask-fMRI approaches to extensively investigate the functional signatures of the different components that characterize the human behavior. First, I will describe a set of experiments, based on temporally controlled task designs, reported in [1], [2] and [3], in which we leverage a collection of source task-fMRI data from the Individual Brain Charting (IBC) dataset. The main goal herein is to investigate the feasibility of performing individual functional brain atlasing, free from inter-subject and inter-site variability, as an effort to establish an univocal relationship between functional segregation of brain regions and elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. In addition, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network. Second, I will describe our ongoing work on the quality-assessment and validation of a subset of tasks from IBC dataset based on naturalistic stimuli using a Fast Shared Response Model encoding experiment [4]. I will finish this presentation with some insights about the application of the aforementioned functional-atlasing techniques to probe region-specific topographies linked to a particular neurocognitive mechanism of interest.
[1] Pinho, A.L. et al. (2021) DOI: 10.1002/hbm.25189
[2] Pinho, A.L. et al. (2018) DOI: 10.1038/sdata.2018.105
[3] Pinho, A.L. et al. (2020) DOI: 10.1038/s41597-020-00670-4
[4] Richard, H. et al. (2019) DOI: 10.48550/arXiv.1909.12537
Deep behavioral phenotyping in functional MRI for cognitive mapping of the hu...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required, by pooling data or results from different single-task studies. Meta-analyses allow the accumulation of knowledge across studies. Yet, they are typically impacted not only by inter-subject and inter-site variability but also loss of information from sparse peak-coordinate representations. In this talk, I will address a battery of studies, which combine deep phenotyping and multitask-fMRI approaches to extensively investigate the functional signatures of the different components that characterize the human behavior. First, I will describe a set of experiments, based on temporally controlled task designs, reported in [1], [2] and [3], in which we leverage a collection of source task-fMRI data from the Individual Brain Charting (IBC) dataset. The main goal herein is to investigate the feasibility of performing individual functional brain atlasing, free from inter-subject and inter-site variability, as an effort to establish an univocal relationship between functional segregation of brain regions and elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. In addition, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network. Second, I will describe our ongoing work on the quality-assessment and validation of a subset of tasks from the IBC dataset based on naturalistic stimuli using two types of encoding models: the unsupervised Fast Shared Response Model [4], and a feature-defined model based on Deep Convolutional Neural Networks [5, 6].
[1] Pinho, A.L. et al. (2021) DOI: 10.1002/hbm.25189
[2] Pinho, A.L. et al. (2018) DOI: 10.1038/sdata.2018.105
[3] Pinho, A.L. et al. (2020) DOI: 10.1038/s41597-020-00670-4
[4] Richard, H. et al. (2019) DOI: 10.48550/arXiv.1909.12537
[5] Eickenberg, M. et al. (2016) DOI: 10.1016/j.neuroimage.2016.10.001
[6] Güçlü, U. and van Gerven, M. A. J. (2015) DOI: 10.1523/JNEUROSCI.5023-14.2015
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
This document summarizes a meeting that discussed analyzing naturalistic fMRI data using the Fast Shared Response Model (FastSRM). It described the Individual Brain Charting dataset, analyzing clips and raiders tasks with FastSRM, evaluating reproducibility through cross-validation, and finding consistent retinotopic maps and semantic space across subjects. The document concluded that FastSRM is a computationally efficient method for analyzing naturalistic fMRI data.
The document discusses how computation can accelerate the generation of new knowledge by enabling large-scale collaborative research and extracting insights from vast amounts of data. It provides examples from astronomy, physics simulations, and biomedical research where computation has allowed more data and researchers to be incorporated, advancing various fields more quickly over time. Computation allows for data sharing, analysis, and hypothesis generation at scales not previously possible.
The document discusses metagenomics analysis tools and challenges. It summarizes several metagenome analysis portals that provide computational analysis and public sample databases. It also discusses the rapid growth of metagenomic data being produced, challenges around quality control, feature identification, characterization and presentation of metagenomic data, and the need for standardized metadata and data formats. The future directions highlighted include studying strain variation, expanding metadata capture and standards, and developing improved assembly, binning and analysis methods.
Design and evaluation of a genomics variant analysis pipeline using GATK Spar...Paolo Missier
A paper presented at the annual Italian Database conference (SEBD): http://sisinflab.poliba.it/sebd/2018/
here is the paper: http://sisinflab.poliba.it/sebd/2018/papers/June-27-Wednesday/1-Big-Data/SEBD_2018_paper_23.pdf
Deep behavioral phenotyping in functional MRI for cognitive mapping of the hu...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required, by pooling data or results from different single-task studies. Meta-analyses allow the accumulation of knowledge across studies. Yet, they are typically impacted not only by inter-subject and inter-site variability but also loss of information from sparse peak-coordinate representations. In this talk, I will address a battery of studies, which combine deep phenotyping and multitask-fMRI approaches to extensively investigate the functional signatures of the different components that characterize the human behavior. First, I will describe a set of experiments, based on temporally controlled task designs, reported in [1], [2] and [3], in which we leverage a collection of source task-fMRI data from the Individual Brain Charting (IBC) dataset. The main goal herein is to investigate the feasibility of performing individual functional brain atlasing, free from inter-subject and inter-site variability, as an effort to establish an univocal relationship between functional segregation of brain regions and elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. In addition, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network. Second, I will describe our ongoing work on the quality-assessment and validation of a subset of tasks from IBC dataset based on naturalistic stimuli using a Fast Shared Response Model encoding experiment [4]. I will finish this presentation with some insights about the application of the aforementioned functional-atlasing techniques to probe region-specific topographies linked to a particular neurocognitive mechanism of interest.
[1] Pinho, A.L. et al. (2021) DOI: 10.1002/hbm.25189
[2] Pinho, A.L. et al. (2018) DOI: 10.1038/sdata.2018.105
[3] Pinho, A.L. et al. (2020) DOI: 10.1038/s41597-020-00670-4
[4] Richard, H. et al. (2019) DOI: 10.48550/arXiv.1909.12537
Deep behavioral phenotyping in functional MRI for cognitive mapping of the hu...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required, by pooling data or results from different single-task studies. Meta-analyses allow the accumulation of knowledge across studies. Yet, they are typically impacted not only by inter-subject and inter-site variability but also loss of information from sparse peak-coordinate representations. In this talk, I will address a battery of studies, which combine deep phenotyping and multitask-fMRI approaches to extensively investigate the functional signatures of the different components that characterize the human behavior. First, I will describe a set of experiments, based on temporally controlled task designs, reported in [1], [2] and [3], in which we leverage a collection of source task-fMRI data from the Individual Brain Charting (IBC) dataset. The main goal herein is to investigate the feasibility of performing individual functional brain atlasing, free from inter-subject and inter-site variability, as an effort to establish an univocal relationship between functional segregation of brain regions and elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. In addition, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network. Second, I will describe our ongoing work on the quality-assessment and validation of a subset of tasks from the IBC dataset based on naturalistic stimuli using two types of encoding models: the unsupervised Fast Shared Response Model [4], and a feature-defined model based on Deep Convolutional Neural Networks [5, 6].
[1] Pinho, A.L. et al. (2021) DOI: 10.1002/hbm.25189
[2] Pinho, A.L. et al. (2018) DOI: 10.1038/sdata.2018.105
[3] Pinho, A.L. et al. (2020) DOI: 10.1038/s41597-020-00670-4
[4] Richard, H. et al. (2019) DOI: 10.48550/arXiv.1909.12537
[5] Eickenberg, M. et al. (2016) DOI: 10.1016/j.neuroimage.2016.10.001
[6] Güçlü, U. and van Gerven, M. A. J. (2015) DOI: 10.1523/JNEUROSCI.5023-14.2015
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
This document summarizes a meeting that discussed analyzing naturalistic fMRI data using the Fast Shared Response Model (FastSRM). It described the Individual Brain Charting dataset, analyzing clips and raiders tasks with FastSRM, evaluating reproducibility through cross-validation, and finding consistent retinotopic maps and semantic space across subjects. The document concluded that FastSRM is a computationally efficient method for analyzing naturalistic fMRI data.
The document discusses how computation can accelerate the generation of new knowledge by enabling large-scale collaborative research and extracting insights from vast amounts of data. It provides examples from astronomy, physics simulations, and biomedical research where computation has allowed more data and researchers to be incorporated, advancing various fields more quickly over time. Computation allows for data sharing, analysis, and hypothesis generation at scales not previously possible.
The document discusses metagenomics analysis tools and challenges. It summarizes several metagenome analysis portals that provide computational analysis and public sample databases. It also discusses the rapid growth of metagenomic data being produced, challenges around quality control, feature identification, characterization and presentation of metagenomic data, and the need for standardized metadata and data formats. The future directions highlighted include studying strain variation, expanding metadata capture and standards, and developing improved assembly, binning and analysis methods.
Design and evaluation of a genomics variant analysis pipeline using GATK Spar...Paolo Missier
A paper presented at the annual Italian Database conference (SEBD): http://sisinflab.poliba.it/sebd/2018/
here is the paper: http://sisinflab.poliba.it/sebd/2018/papers/June-27-Wednesday/1-Big-Data/SEBD_2018_paper_23.pdf
Genome in a Bottle - Towards new benchmarks for the “dark matter” of the huma...GenomeInABottle
The document discusses Genome in a Bottle (GIAB) and its efforts to characterize human genomes and provide reference materials and benchmarks to evaluate genome sequencing and variant calling. Specifically, it summarizes how GIAB has characterized 7 human genomes, provides extensive public sequencing data for benchmarking, and is now using linked and long reads to expand the small variant benchmark set, develop a structural variant benchmark, and perform diploid assembly of difficult regions. It also shows how new benchmarks that include more difficult regions have revealed errors in previous benchmarks and reduced performance metrics for variant calling tools.
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
Feature Extraction for Large-Scale Text CollectionsSease
Feature engineering is a fundamental but poorly documented component in LTR search applications.
As a result, there are still few open access software packages that allow researchers and practitioners to easily simulate a feature extraction pipeline and conduct experiments in a lab setting.
This talk introduces Fxt, an open-source framework to perform efficient and scalable feature extraction. Fxt may be integrated into complex, high-performance software applications to help solve a wide variety of text-based machine learning problems.
The talk details how we built and documented a reproducible feature extraction pipeline with LTR experiments using the ClueWeb09B collection.
This LTR dataset is publicly available.
We’ll also discuss some of the benefits (feature extraction efficiency, model interpretation) of having open access tooling in this area for researchers and practitioners alike.
MOCHA Challenge was hosted by European Semantic Web Conference ESWC, 3-7 June 2018, held in Heraklion, Crete, Greece (Aldemar Knossos Royal & Royal Villa).
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
This document summarizes a presentation on using convolutional deep belief networks (CDBNs) for unsupervised feature learning from audio data. It describes CDBNs and how they are composed of convolutional restricted Boltzmann machines trained in a greedy layer-wise fashion. It then discusses how CDBNs were applied to unlabeled speech and music audio clips to learn hierarchical representations, which were then evaluated on speech recognition and music classification tasks. The results showed the CDBN-learned features outperformed raw audio features and MFCC features.
Metabolomic Data Analysis Workshop and Tutorials (2014)Dmitry Grapov
This document provides an introduction and overview of tutorials for metabolomic data analysis. It discusses downloading required files and software. The goals of the analysis include using statistical and multivariate analyses to identify differences between sample groups and impacted biochemical domains. It also discusses various data analysis techniques including data quality assessment, univariate and multivariate statistical analyses, clustering, principal component analysis, partial least squares modeling, functional enrichment analysis, and network mapping.
OVium Bio-Information Solutions use forefront algorithms to analyze key data resources such NCBI, EBLM and PDB to develop cell signal pathways.
OVium employs cloud and MPP computing solutions with homology and signal network mapping to develop chemical and protein pathways for discovery research.
The document discusses data curation from data lakes. It describes the data lake paradigm of collecting all data and making it searchable. It then discusses the importance of data curation and normalization to generate value from large and diverse datasets. Examples are provided showing how sample annotations can be normalized and structured to enable complex queries across multiple datasets. The document reflects on challenges around quantifying the value of data curation and need for curation as data volumes increase.
This document compares two solutions for filtering hierarchical data sets: Solution A uses MySQL and Python, while Solution B uses MongoDB and C++. Both solutions were tested on a 2011 MeSH data set using various filtering methods and thresholds. Solution A generally had faster execution times at lower thresholds, while Solution B scaled better to higher thresholds. However, the document concludes that neither solution is clearly superior, and further study is needed to evaluate their performance for real-world human users.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
This presentation discusses standards for sharing functional genomics data. It summarizes lessons learned from the Minimum Information About a Microarray Experiment (MIAME) standard, including that simply depositing data is not enough - metadata, analysis code, and usable formats are also needed for reproducibility. For high-throughput sequencing data, a Minimum Information about a high-throughput Nucleotide Sequencing Experiment (MINSEQE) standard is proposed with similar requirements as MIAME. The presentation emphasizes keeping standards simple while ensuring machine-readability for reuse.
1) Scientists at the Advanced Photon Source use the Argonne Leadership Computing Facility for data reconstruction and analysis from experimental facilities in real-time or near real-time. This provides feedback during experiments.
2) Using the Swift parallel scripting language and ALCF supercomputers like Mira, scientists can process terabytes of data from experiments in minutes rather than hours or days. This enables errors to be detected and addressed during experiments.
3) Key applications discussed include near-field high-energy X-ray diffraction microscopy, X-ray nano/microtomography, and determining crystal structures from diffuse scattering images through simulation and optimization. The workflows developed provide significant time savings and improved experimental outcomes.
Real-Time Pertinent Maneuver Recognition for SurveillanceIRJET Journal
This document presents a real-time system for recognizing pertinent maneuvers and detecting weapons using video surveillance. The system uses a ResNet-34 model trained on the Kinetics 400 dataset to recognize human activities in real-time video streams. It also uses a YOLOv4 model trained on a custom dataset to detect weapons. The ResNet-34 model can analyze still images and live video streams to classify activities. YOLOv4 is used for fast and accurate object detection of weapons. The system is designed to be deployed in CCTV surveillance to recognize activities and detect if individuals are carrying weapons. It aims to provide timely notifications or advanced user information for real-time monitoring.
Supporting image-based meta-analysis with NIDM: Standardized reporting of neu...Camille Maumet
Due to the lack of data shared when reporting neuroimaging results, most neuroimaging meta-analyses are based on peak coordinate data. However, the best practice is an image-based meta-analysis that combines full image data of the effect estimates and standard errors derived from each study.
The Neuroimaging Data Model (NIDM) is an ongoing effort, supported by the INCF, to provide a domain-specific extension of the W3C PROV-DM.
In this talk, I will review our recent progress in extending NIDM to share the statistical results of a neuroimaging study and our interactions with existing software packages (SPM, FSL, AFNI, Neurovault.org).
SAFE: Policy Aware SPARQL Query Federation Over RDF Data CubesRatnesh Sahay
- Dr. Ratnesh Sahay presented on the Policy Aware SPARQL Query Federation Over RDF Data Cubes project called SAFE at the SWAT4LS-2014 conference in Berlin, Germany.
- SAFE allows for querying across multiple RDF data cube datasets while respecting access policies through policy-aware query rewriting and execution.
- An evaluation showed SAFE performed more efficiently than existing federated query systems, with significantly faster source selection times and ability to execute queries that timed out on other systems.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
This document provides an overview of next generation sequencing (NGS) analysis. It discusses various NGS platforms such as Illumina, Roche 454, PacBio, and Ion Torrent. It also covers common file formats for sequencing data like FASTQ, quality control measures to assess data quality, and applications of NGS such as RNA-seq and ChIP-seq. The document aims to introduce researchers to basic concepts in NGS analysis and highlights available resources for storing and analyzing large sequencing datasets.
Micro B3 Information System and Biovel: Resources, Services, Workflows and In...Renzo Kottmann
This document summarizes resources, services, workflows and interfaces provided by the Micro B3 Information System and BioVeL for microbial genomics and bioinformatics research. Key points include:
- The Micro B3 Information System provides databases, analysis tools, and metagenomic workflows for functional trait-based analysis of aquatic microbial communities.
- Services include RESTful APIs, web mapping services, tools for data access and analysis, and ecological analysis tools.
- Workflows integrate sample collection, sequencing, analysis and metadata submission from projects like Ocean Sampling Day.
- Interfaces include web and mobile applications for data access, visualization and field data collection.
- The systems are open source and collaboratively developed.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
How to design stimulus presentation for a task-fMRI experimentAna Luísa Pinho
The document discusses how to design stimulus presentation for a task-fMRI experiment. It explains that the goal is to induce psychological states in the subject and detect related brain signals. There are two main types of experimental designs: blocked designs, where similar events are grouped, and event-related designs, where events are mixed. Key psychological considerations for stimulus design include maintaining stimulus predictability, maximizing time on task, and avoiding unintended psychological activity. The level of predictability influences a subject's psychological state, and short stimuli are recommended to keep subjects engaged in the intended task.
More Related Content
Similar to Deep behavioral phenotyping in functional MRI for cognitive mapping of the human brain
Genome in a Bottle - Towards new benchmarks for the “dark matter” of the huma...GenomeInABottle
The document discusses Genome in a Bottle (GIAB) and its efforts to characterize human genomes and provide reference materials and benchmarks to evaluate genome sequencing and variant calling. Specifically, it summarizes how GIAB has characterized 7 human genomes, provides extensive public sequencing data for benchmarking, and is now using linked and long reads to expand the small variant benchmark set, develop a structural variant benchmark, and perform diploid assembly of difficult regions. It also shows how new benchmarks that include more difficult regions have revealed errors in previous benchmarks and reduced performance metrics for variant calling tools.
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
Feature Extraction for Large-Scale Text CollectionsSease
Feature engineering is a fundamental but poorly documented component in LTR search applications.
As a result, there are still few open access software packages that allow researchers and practitioners to easily simulate a feature extraction pipeline and conduct experiments in a lab setting.
This talk introduces Fxt, an open-source framework to perform efficient and scalable feature extraction. Fxt may be integrated into complex, high-performance software applications to help solve a wide variety of text-based machine learning problems.
The talk details how we built and documented a reproducible feature extraction pipeline with LTR experiments using the ClueWeb09B collection.
This LTR dataset is publicly available.
We’ll also discuss some of the benefits (feature extraction efficiency, model interpretation) of having open access tooling in this area for researchers and practitioners alike.
MOCHA Challenge was hosted by European Semantic Web Conference ESWC, 3-7 June 2018, held in Heraklion, Crete, Greece (Aldemar Knossos Royal & Royal Villa).
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
This document summarizes a presentation on using convolutional deep belief networks (CDBNs) for unsupervised feature learning from audio data. It describes CDBNs and how they are composed of convolutional restricted Boltzmann machines trained in a greedy layer-wise fashion. It then discusses how CDBNs were applied to unlabeled speech and music audio clips to learn hierarchical representations, which were then evaluated on speech recognition and music classification tasks. The results showed the CDBN-learned features outperformed raw audio features and MFCC features.
Metabolomic Data Analysis Workshop and Tutorials (2014)Dmitry Grapov
This document provides an introduction and overview of tutorials for metabolomic data analysis. It discusses downloading required files and software. The goals of the analysis include using statistical and multivariate analyses to identify differences between sample groups and impacted biochemical domains. It also discusses various data analysis techniques including data quality assessment, univariate and multivariate statistical analyses, clustering, principal component analysis, partial least squares modeling, functional enrichment analysis, and network mapping.
OVium Bio-Information Solutions use forefront algorithms to analyze key data resources such NCBI, EBLM and PDB to develop cell signal pathways.
OVium employs cloud and MPP computing solutions with homology and signal network mapping to develop chemical and protein pathways for discovery research.
The document discusses data curation from data lakes. It describes the data lake paradigm of collecting all data and making it searchable. It then discusses the importance of data curation and normalization to generate value from large and diverse datasets. Examples are provided showing how sample annotations can be normalized and structured to enable complex queries across multiple datasets. The document reflects on challenges around quantifying the value of data curation and need for curation as data volumes increase.
This document compares two solutions for filtering hierarchical data sets: Solution A uses MySQL and Python, while Solution B uses MongoDB and C++. Both solutions were tested on a 2011 MeSH data set using various filtering methods and thresholds. Solution A generally had faster execution times at lower thresholds, while Solution B scaled better to higher thresholds. However, the document concludes that neither solution is clearly superior, and further study is needed to evaluate their performance for real-world human users.
Ijricit 01-002 enhanced replica detection in short time for large data setsIjripublishers Ijri
Similarity check of real world entities is a necessary factor in these days which is named as Data Replica Detection.
Time is an critical factor today in tracking Data Replica Detection for large data sets, without having impact over quality
of Dataset. In this we primarily introduce two Data Replica Detection algorithms , where in these contribute enhanced
procedural standards in finding Data Replication at limited execution periods.This contribute better improvised state
of time than conventional techniques . We propose two Data Replica Detection algorithms namely progressive sorted
neighborhood method (PSNM), which performs best on small and almost clean datasets, and progressive blocking (PB),
which performs best on large and very grimy datasets. Both enhance the efficiency of duplicate detection even on very
large datasets.
This presentation discusses standards for sharing functional genomics data. It summarizes lessons learned from the Minimum Information About a Microarray Experiment (MIAME) standard, including that simply depositing data is not enough - metadata, analysis code, and usable formats are also needed for reproducibility. For high-throughput sequencing data, a Minimum Information about a high-throughput Nucleotide Sequencing Experiment (MINSEQE) standard is proposed with similar requirements as MIAME. The presentation emphasizes keeping standards simple while ensuring machine-readability for reuse.
1) Scientists at the Advanced Photon Source use the Argonne Leadership Computing Facility for data reconstruction and analysis from experimental facilities in real-time or near real-time. This provides feedback during experiments.
2) Using the Swift parallel scripting language and ALCF supercomputers like Mira, scientists can process terabytes of data from experiments in minutes rather than hours or days. This enables errors to be detected and addressed during experiments.
3) Key applications discussed include near-field high-energy X-ray diffraction microscopy, X-ray nano/microtomography, and determining crystal structures from diffuse scattering images through simulation and optimization. The workflows developed provide significant time savings and improved experimental outcomes.
Real-Time Pertinent Maneuver Recognition for SurveillanceIRJET Journal
This document presents a real-time system for recognizing pertinent maneuvers and detecting weapons using video surveillance. The system uses a ResNet-34 model trained on the Kinetics 400 dataset to recognize human activities in real-time video streams. It also uses a YOLOv4 model trained on a custom dataset to detect weapons. The ResNet-34 model can analyze still images and live video streams to classify activities. YOLOv4 is used for fast and accurate object detection of weapons. The system is designed to be deployed in CCTV surveillance to recognize activities and detect if individuals are carrying weapons. It aims to provide timely notifications or advanced user information for real-time monitoring.
Supporting image-based meta-analysis with NIDM: Standardized reporting of neu...Camille Maumet
Due to the lack of data shared when reporting neuroimaging results, most neuroimaging meta-analyses are based on peak coordinate data. However, the best practice is an image-based meta-analysis that combines full image data of the effect estimates and standard errors derived from each study.
The Neuroimaging Data Model (NIDM) is an ongoing effort, supported by the INCF, to provide a domain-specific extension of the W3C PROV-DM.
In this talk, I will review our recent progress in extending NIDM to share the statistical results of a neuroimaging study and our interactions with existing software packages (SPM, FSL, AFNI, Neurovault.org).
SAFE: Policy Aware SPARQL Query Federation Over RDF Data CubesRatnesh Sahay
- Dr. Ratnesh Sahay presented on the Policy Aware SPARQL Query Federation Over RDF Data Cubes project called SAFE at the SWAT4LS-2014 conference in Berlin, Germany.
- SAFE allows for querying across multiple RDF data cube datasets while respecting access policies through policy-aware query rewriting and execution.
- An evaluation showed SAFE performed more efficiently than existing federated query systems, with significantly faster source selection times and ability to execute queries that timed out on other systems.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
This document provides an overview of next generation sequencing (NGS) analysis. It discusses various NGS platforms such as Illumina, Roche 454, PacBio, and Ion Torrent. It also covers common file formats for sequencing data like FASTQ, quality control measures to assess data quality, and applications of NGS such as RNA-seq and ChIP-seq. The document aims to introduce researchers to basic concepts in NGS analysis and highlights available resources for storing and analyzing large sequencing datasets.
Micro B3 Information System and Biovel: Resources, Services, Workflows and In...Renzo Kottmann
This document summarizes resources, services, workflows and interfaces provided by the Micro B3 Information System and BioVeL for microbial genomics and bioinformatics research. Key points include:
- The Micro B3 Information System provides databases, analysis tools, and metagenomic workflows for functional trait-based analysis of aquatic microbial communities.
- Services include RESTful APIs, web mapping services, tools for data access and analysis, and ecological analysis tools.
- Workflows integrate sample collection, sequencing, analysis and metadata submission from projects like Ocean Sampling Day.
- Interfaces include web and mobile applications for data access, visualization and field data collection.
- The systems are open source and collaboratively developed.
Similar to Deep behavioral phenotyping in functional MRI for cognitive mapping of the human brain (20)
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
How to design stimulus presentation for a task-fMRI experimentAna Luísa Pinho
The document discusses how to design stimulus presentation for a task-fMRI experiment. It explains that the goal is to induce psychological states in the subject and detect related brain signals. There are two main types of experimental designs: blocked designs, where similar events are grouped, and event-related designs, where events are mixed. Key psychological considerations for stimulus design include maintaining stimulus predictability, maximizing time on task, and avoiding unintended psychological activity. The level of predictability influences a subject's psychological state, and short stimuli are recommended to keep subjects engaged in the intended task.
How to conduct and fMRI experiment in cognitive neuroscienceAna Luísa Pinho
This document outlines the steps to conduct an fMRI experiment in cognitive neuroscience. It discusses prescreening participants and obtaining consent during recruitment. It describes the MRI scanner setup, including the control room and placing participants inside the scanner. The key steps of an experiment are running a localizer scan, anatomical scan, setting the field of view, and running the functional EPI sequence. Resources provided include Western's ethics guidelines and a textbook on fMRI.
Journal Club - "Intermediate acoustic-to-semantic representations link behavi...Ana Luísa Pinho
Revisiting publication at Nature Neuroscience in 2023 by Giordani, B. L. et al. on "Intermediate acoustic-to-semantic representations link behavioral and neural responses to natural sounds"
Music SDTB: Probing the Neurocognitive Mechanisms of Temporal PredictionsAna Luísa Pinho
This document outlines a study called the Music Single-Domain Task Battery (Music-SDTB) that aims to investigate the neurocognitive mechanisms underlying temporal predictions. The study will test 25 participants using 6 tasks that manipulate timing and sensory domains to probe the contributions of the basal ganglia and cerebellum in forming temporal predictions in rhythmic and non-rhythmic sequences. Key hypotheses are that the basal ganglia contributes more to rhythmic timing judgments while the cerebellum contributes more to single interval timing judgments. Results will provide insights into the role of different brain regions and sensory modalities in temporal prediction.
Revisiting publication at Nature Neuroscience in 2009 by Kriegeskorte, N. et al. on "Circular analysis in systems neuroscience: the dangers of double dipping"
Single-Domain Task Battery (SDTB) on Temporal PredictionAna Luísa Pinho
This document summarizes a single-domain task battery (SDTB) study on temporal prediction using fMRI. The study aims to assess whether temporal predictions are mediated by context-specific or common neurocognitive mechanisms. It outlines three models of temporal prediction and references a behavioral study that found a double dissociation between rhythmic and single-interval predictions, implicating separate roles of the cerebellum and basal ganglia. The SDTB study will use different tasks manipulating timing and sensory domains to investigate the neural correlates associated with these sub-cortical contributions and their relationship to cortex. Participants will complete production and perception tasks with auditory and visual stimuli in beat and interval conditions.
Individual functional atlasing of the human brain with multitask fMRI data: l...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. The Individual Brain Charting (IBC) project stands for a high-resolution (1.5mm), multi-task fMRI dataset, intended to provide an objective basis for the establishment of a neurocognitive atlas based on the individual mapping of the human brain. This data collection refers to a permanent cohort during performance of a wide variety of tasks across many sessions. Data up to the third release---comprising 28 tasks---are publicly available in the OpenNeuro repository (ds002685). Derived statistical maps from the first and second releases can be found in NeuroVault (id6618) and they amount for 205 canonical contrasts described on the basis of 113 cognitive concepts taken from the Cognitive Atlas. These derivatives reveal all together a comprehensive brain coverage of regions engaged in cognitive processes as well as a successful encoding of the functional networks reported by the original studies. As the dataset becomes larger and the ensuing collection of concepts gets richer, finer subject-specific, cognitive topographies can be extracted from the data. We thus explore this individual-functional-atlasing approach in order to link functional segregation of specialized brain regions to elementary mental functions. Results show that individual topographies---common to all tasks---are consistently mapped within and, to a lesser extent, across participants. Besides, prediction scores associated with the reconstruction of contrasts of one task from the remaining ones reveal the quantitative contribution of each task to these common representations. Yet, scores decreased when subjects were permuted between train and test, confirming that topographies are driven by subject-specific variability. Lastly, we demonstrate how cognitive mapping can benefit from contrasts accumulation, by analyzing the functional fingerprints of a set of individualized regions-of-interest from the language network.
Segregation of functional territories in individual brainsAna Luísa Pinho
Aims
FMRI allows for characterization of brain activations in response to behavior. However, cognitive neuroscience is limited to group-level effects on specific tasks. Pooling data from different task-fMRI studies free from inter-subject and inter-site variability is mandatory toward a fine functional profile of cognitive atoms. We present the Individual Brain Charting dataset --concerning fMRI data acquired at high resolution (1.5mm) in the same environment and cohort-- and investigate the feasibility of individual functional atlasing using a rich taskwise dataset.
Methods
Individual z-maps from the 60 main contrasts across tasks were estimated to capture significant functional signatures. These derivatives were analyzed as in Pinho et al. (2018). Besides, contrasts were decomposed using dictionary-learning into individual networks featuring neural correlates common to the tasks. To gain insights about these commonalities, we also reconstructed contrasts from the remaining ones in a cross-validation experiment. Additionally, we delineated the cognitive profile of 6 regions-of-interest and assessed whether voxels were correctly assigned to these regions across participants.
Results
Individual components were consistently mapped and tasks were well predicted from one another. Yet, scores decreased when subjects were permuted between train and test, showing that topographies are driven by subject-specific anatomo-functional characteristics. Additionally, characterization of regions-of-interest from many contrasts objectively establishes functional specialization, supported by prediction accuracies of voxel classification.
Conclusions
Successful predictions revealed the existence of a latent structure underlying different tasks, illustrating the benefit of system-level, multi-task brain mapping. Contrasts and, subsequently, individual topographies are increasing with the latest releases, allowing for better brain-atlasing frameworks.
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. To date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) dataset stands for a high-resolution multi-task fMRI dataset that intends to provide a framework toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort (12 participants) performing many different tasks, free from both inter-subject and inter-site variability. The first release of the IBC dataset is already out and publicly available in the OpenNeuro (ds000244) and NeuroVault (id=4438). These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain, as the main findings of the original studies were reproduced at a high resolution. They thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. Additionally, this dataset supports the investigation of mega-analytic encoding models to be implemented in a brain-atlasing infrastructure, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault [1]. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Functional specialization in human cognition: a large-scale neuroimaging init...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Functional specialization in human cognition: a large-scale neuroimaging init...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault [1]. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Ana Luísa Pinho
Mapping functional neuroanatomy of the human brain has become a central challenge in cognitive neuroscience and it constitutes an essential step toward linking brain systems and behavior. While there is a rich literature on the neural correlates underlying performance of standardized tasks, little is still known about the overall functional organization of the brain and how it can be translated into cognition. Neuroimaging techniques, such as Functional Magnetic Resonance Imaging (fMRI) have contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms within a broader scope. The Individual Brain Charting (IBC) project stands for a multi-task fMRI dataset, to be shared with the neuroimaging community, featuring an univocal encoding between task descriptors and brain imaging data. It is intended to support the investigation of the functional principles underlying the cognitive representation of the human brain, allowing for (e.g.) a detailed parcellation of the brain volume into functional-specialized regions. Additionally, the IBC project pertains to the development of a neurocognitive atlas based on the functional signatures of mutual cognitive components between task descriptors.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Deep behavioral phenotyping in functional MRI for cognitive mapping of the human brain
1. @ALuisaPinho@fediscience.org
@ALuisaPinho Seminar at the Cognitive Science Lab
Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Deep behavioral phenotyping in functional MRI for
cognitive mapping of the human brain
Ana Luı́sa Pinho, Ph.D.
BrainsCAN Postdoctoral Fellow
Western University, London Ontario, Canada
This work was developed in the Parietal Team at NeuroSpin/Inria-Saclay, Paris, France.
22nd of February, 2023
2. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Overview of the Individual Brain Charting (IBC) dataset
Data-quality assessment of IBC
Individual Functional Atlasing leveraging IBC First-Release
Encoding analysis of naturalistic stimuli from IBC Third-Release using
the Fast Shared Response Model (FastSRM)
Future perspectives: mapping cognitive-specific mechanisms
2/35
3. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Overview of the IBC dataset
4. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
4/ 35
5. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
Experiments typically shall:
tackle one psychological domain
4/ 35
6. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
Experiments typically shall:
tackle one psychological domain
be specific enough to accurately isolate brain processes
4/ 35
7. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
Experiments typically shall:
tackle one psychological domain
be specific enough to accurately isolate brain processes
⇓
Very hard to achieve!
Lack of generality.
8. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (1/2)
In cognitive neuroscience:
Brain systems
⇐⇒
Mental functions
Task-fMRI experiments allow to:
link brain systems to behavior
map neural activity at mm-scale
4/ 35
9. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings cognitive annotations
low inter-subject variability sufficient multi-task data
5/ 35
10. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings cognitive annotations
low inter-subject variability sufficient multi-task data
5/ 35
11. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings ( ) cognitive annotations
low inter-subject variability sufficient multi-task data
5/ 35
12. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings ( ) cognitive annotations
low inter-subject variability sufficient multi-task data
Large-scale repositories:
OpenNeuro
NeuroVault
EBRAINS
5/ 35
13. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings ( ) cognitive annotations
low inter-subject variability sufficient multi-task data
Large-scale repositories:
OpenNeuro
NeuroVault
EBRAINS
Individual analysis:
Fedorenko, E. et al. (2011)
Haxby, J. et al. (2011)
Hanke, M. et al. (2014)
5/ 35
14. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings ( )( ) cognitive annotations
low inter-subject variability sufficient multi-task data
Large-scale repositories:
OpenNeuro
NeuroVault
EBRAINS
Individual analysis:
Fedorenko, E. et al. (2011)
Haxby, J. et al. (2011)
Hanke, M. et al. (2014)
Large-scale datasets:
HCP
studyforrest
CONNECT/Archi
5/ 35
15. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Background and motivations (2/2)
Data-pooling analysis
Meta-analysis:
pooling data derivatives
Mega-analysis:
pooling raw data
Requisites for cognitive mapping
Minimize variability of Successful interpretation of
spatial location combined results
same processing no loss of info from sparse
routines peak-coord. representation
same experimental consistency of
settings ( )( ) cognitive annotations
low inter-subject variability sufficient multi-task data
Large-scale repositories:
OpenNeuro
NeuroVault
EBRAINS
Individual analysis:
Fedorenko, E. et al. (2011)
Haxby, J. et al. (2011)
Hanke, M. et al. (2014)
Large-scale datasets:
HCP
studyforrest
CONNECT/Archi
The IBC dataset meets all
together the requisites for
cognitive mapping.
5/ 35
16. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
6/ 35
17. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
TR = 2s
6/ 35
18. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
TR = 2s
Task-wise dataset:
Many tasks
6/ 35
19. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
TR = 2s
Task-wise dataset:
Many tasks
Fixed cohort - 12 healthy adults
6/ 35
20. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
TR = 2s
Task-wise dataset:
Many tasks
Fixed cohort - 12 healthy adults
Fixed environment
NeuroSpin platform, CEA-Saclay, France
Siemens 3T Magnetom Prismafit
64-channel coil
6/ 35
21. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
TR = 2s
Task-wise dataset:
Many tasks
Fixed cohort - 12 healthy adults
Fixed environment
Inclusion of other MRI modalities
NeuroSpin platform, CEA-Saclay, France
Siemens 3T Magnetom Prismafit
64-channel coil
6/ 35
22. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
The IBC dataset
High spatial-resolution fMRI data (1.5mm)
TR = 2s
Task-wise dataset:
Many tasks
Fixed cohort - 12 healthy adults
Fixed environment
Inclusion of other MRI modalities
Not a longitudinal study!
NeuroSpin platform, CEA-Saclay, France
Siemens 3T Magnetom Prismafit
64-channel coil
6/ 35
23. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Analysis pipeline
7/ 35
24. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Data-quality assessment of
the IBC First-Release
25. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Tasks of the First Release
▶ ARCHI tasks
Standard
Spatial
Social
Emotional
▶ HCP tasks
Emotion
Gambling
Motor
Language
Relational
Social
Working Memory
▶ RSVP Language
9/ 35
26. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Tasks of the First Release
▶ ARCHI tasks
Standard
Spatial
Social
Emotional
▶ HCP tasks
Emotion
Gambling
Motor
Language
Relational
Social
Working Memory
▶ RSVP Language
▶ Sensory processing:
Retinotopy
Tonotopy
Somatotopy
▶ High-cognitive order:
Calculation
Language
Social cognition
Theory-of-mind
9/ 35
27. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Tasks of the First Release
▶ ARCHI tasks
Standard
Spatial
Social
Emotional
▶ HCP tasks
Emotion
Gambling
Motor
Language
Relational
Social
Working Memory
▶ RSVP Language
▶ Sensory processing:
Retinotopy
Tonotopy
Somatotopy
▶ High-cognitive order:
Calculation
Language
Social cognition
Theory-of-mind
All contrasts: 119
Elementary contrasts: 59
9/ 35
28. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
IBC reproduces ARCHI and HCP
t
a
l
e
v
s
.
m
e
n
t
a
l
a
d
d
i
t
i
o
n
m
e
n
t
a
l
m
o
t
i
o
n
v
s
.
r
a
n
d
o
m
m
o
t
i
o
n
p
u
n
i
s
h
m
e
n
t
v
s
.
r
e
w
a
r
d
l
e
f
t
f
o
o
t
v
s
.
a
n
y
m
o
t
i
o
n
l
e
f
t
h
a
n
d
v
s
.
a
n
y
m
o
t
i
o
n
r
i
g
h
t
f
o
o
t
v
s
.
a
n
y
m
o
t
i
o
n
r
i
g
h
t
h
a
n
d
v
s
.
a
n
y
m
o
t
i
o
n
t
o
n
g
u
e
v
s
.
a
n
y
m
o
t
i
o
n
f
a
c
e
i
m
a
g
e
v
s
.
s
h
a
p
e
o
u
t
l
i
n
e
r
e
l
a
t
i
o
n
a
l
p
r
o
c
e
s
s
i
n
g
v
s
.
v
i
s
u
a
l
m
a
t
c
h
i
n
g
2
-
b
a
c
k
v
s
.
0
-
b
a
c
k
b
o
d
y
i
m
a
g
e
v
s
.
a
n
y
i
m
a
g
e
f
a
c
e
i
m
a
g
e
v
s
.
a
n
y
i
m
a
g
e
p
l
a
c
e
i
m
a
g
e
v
s
.
a
n
y
i
m
a
g
e
t
o
o
l
i
m
a
g
e
v
s
.
a
n
y
i
m
a
g
e
h
o
r
i
z
o
n
t
a
l
c
h
e
c
k
e
r
b
o
a
r
d
v
s
.
v
e
r
t
i
c
a
l
c
h
e
c
k
e
r
b
o
a
r
d
m
e
n
t
a
l
s
u
b
t
r
a
c
t
i
o
n
v
s
.
s
e
n
t
e
n
c
e
r
e
a
d
s
e
n
t
e
n
c
e
v
s
.
l
i
s
t
e
n
t
o
s
e
n
t
e
n
c
e
r
e
a
d
s
e
n
t
e
n
c
e
v
s
.
c
h
e
c
k
e
r
b
o
a
r
d
l
e
f
t
h
a
n
d
v
s
.
r
i
g
h
t
h
a
n
d
s
a
c
c
a
d
e
v
s
.
f
i
x
a
t
i
o
n
g
u
e
s
s
w
h
i
c
h
h
a
n
d
v
s
.
h
a
n
d
p
a
l
m
o
r
b
a
c
k
o
b
j
e
c
t
g
r
a
s
p
i
n
g
v
s
.
m
i
m
i
c
o
r
i
e
n
t
a
t
i
o
n
m
e
n
t
a
l
m
o
t
i
o
n
v
s
.
r
a
n
d
o
m
m
o
t
i
o
n
f
a
l
s
e
-
b
e
l
i
e
f
s
t
o
r
y
v
s
.
m
e
c
h
a
n
i
s
t
i
c
s
t
o
r
y
f
a
l
s
e
-
b
e
l
i
e
f
t
a
l
e
v
s
.
m
e
c
h
a
n
i
s
t
i
c
t
a
l
e
f
a
c
e
t
r
u
s
t
y
v
s
.
f
a
c
e
g
e
n
d
e
r
e
x
p
r
e
s
s
i
o
n
i
n
t
e
n
t
i
o
n
v
s
.
e
x
p
r
e
s
s
i
o
n
g
e
n
d
e
r
tale vs. mental addition
mental motion vs. random motion
punishment vs. reward
left foot vs. any motion
left hand vs. any motion
right foot vs. any motion
right hand vs. any motion
tongue vs. any motion
face image vs. shape outline
relational processing vs. visual matching
2-back vs. 0-back
body image vs. any image
face image vs. any image
place image vs. any image
tool image vs. any image
horizontal checkerboard vs. vertical checkerboard
mental subtraction vs. sentence
read sentence vs. listen to sentence
read sentence vs. checkerboard
left hand vs. right hand
saccade vs. fixation
guess which hand vs. hand palm or back
object grasping vs. mimic orientation
mental motion vs. random motion
false-belief story vs. mechanistic story
false-belief tale vs. mechanistic tale
face trusty vs. face gender
expression intention vs. expression gender
HCP contrasts ARCHI contrasts
IBC
contrasts
1.00
0.75
0.50
0.25
0.00
0.25
0.50
0.75
1.00 ARCHI batteries:
Pinel, P. et al. (2007)
HCP batteries:
Barch, D. M. et al. (2013)
n = 13
Pinho, A.L. et al. Hum Brain Mapp(2021) 10/ 35
29. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Effect of subject and task on brain activity
Per-voxel one-way ANOVA qFDR < 0.05
x=10
L R
z=10 -28
-14
0
14
28
L R
y=-50
Subject effect
x=10
L R
z=10 -37
-19
0
19
37
L R
y=-50
Condition effect
x=-6
L R
z=3 -12
-5.9
0
5.9
12
L R
y=45
Phase encoding effect
Pinho, A.L. et al. SciData(2018)
11/ 35
30. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Effect of subject and task on brain activity
Per-voxel one-way ANOVA qFDR < 0.05
x=10
L R
z=10 -28
-14
0
14
28
L R
y=-50
Subject effect
x=10
L R
z=10 -37
-19
0
19
37
L R
y=-50
Condition effect
x=-6
L R
z=3 -12
-5.9
0
5.9
12
L R
y=45
Phase encoding effect
Pinho, A.L. et al. SciData(2018)
IBC data is suitable for cognitive mapping and
individual-brain modeling!
11/ 35
31. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Activation similarity fits task similarity
Similarity between
activation maps
of elementary contrasts
a
r
c
h
i
e
m
o
t
i
o
n
a
l
a
r
c
h
i
s
o
c
i
a
l
a
r
c
h
i
s
p
a
t
i
a
l
a
r
c
h
i
s
t
a
n
d
a
r
d
h
c
p
e
m
o
t
i
o
n
h
c
p
g
a
m
b
l
i
n
g
h
c
p
l
a
n
g
u
a
g
e
h
c
p
m
o
t
o
r
h
c
p
r
e
l
a
t
i
o
n
a
l
h
c
p
s
o
c
i
a
l
h
c
p
w
m
r
s
v
p
l
a
n
g
u
a
g
e
archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Similarity between
cognitive description
of elementary contrasts
a
r
c
h
i
e
m
o
t
i
o
n
a
l
a
r
c
h
i
s
o
c
i
a
l
a
r
c
h
i
s
p
a
t
i
a
l
a
r
c
h
i
s
t
a
n
d
a
r
d
h
c
p
e
m
o
t
i
o
n
h
c
p
g
a
m
b
l
i
n
g
h
c
p
l
a
n
g
u
a
g
e
h
c
p
m
o
t
o
r
h
c
p
r
e
l
a
t
i
o
n
a
l
h
c
p
s
o
c
i
a
l
h
c
p
w
m
r
s
v
p
l
a
n
g
u
a
g
e
archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Pinho, A.L. et al. SciData(2018)
12/ 35
32. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Similarity between
activation maps of
elementary contrasts
a
r
c
h
i
e
m
o
t
io
n
a
l
a
r
c
h
i
s
o
c
ia
l
a
r
c
h
i
s
p
a
t
ia
l
a
r
c
h
i
s
t
a
n
d
a
r
d
h
c
p
e
m
o
t
io
n
h
c
p
g
a
m
b
li
n
g
h
c
p
la
n
g
u
a
g
e
h
c
p
m
o
t
o
r
h
c
p
r
e
la
t
io
n
a
l
h
c
p
s
o
c
ia
l
h
c
p
w
m
r
s
v
p
la
n
g
u
a
g
e
archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Similarity between
cognitive description
of elementary contrasts
a
r
c
h
i
e
m
o
t
io
n
a
l
a
r
c
h
i
s
o
c
ia
l
a
r
c
h
i
s
p
a
t
ia
l
a
r
c
h
i
s
t
a
n
d
a
r
d
h
c
p
e
m
o
t
io
n
h
c
p
g
a
m
b
li
n
g
h
c
p
la
n
g
u
a
g
e
h
c
p
m
o
t
o
r
h
c
p
r
e
la
t
io
n
a
l
h
c
p
s
o
c
ia
l
h
c
p
w
m
r
s
v
p
la
n
g
u
a
g
e
archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Pinho, A.L. et al. SciData(2018)
Pinho, A.L. et al. SciData(2020)
Second release:
Mental Time Travel battery
Gauthier, B., & van Wassenhove, V. (2016a,b)
Preference battery
Lebreton, M. et al. (2015)
ToM + Pain Matrices battery
Dodell-Feder, D. et al. (2010)
Jacoby, N. et al. (2015)
Richardson, H. et al. (2018)
Visual Short-Term Memory + Enumeration
tasks
Knops, A. et al. (2014)
Self-Reference Effect task
Genon, S. et al. (2014)
“Bang!” task
Campbell, K. L. et al. (2015)
First + Second releases:
All contrasts: 279
Elementary contrasts: 127
13/ 35
33. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Similarity between
activation maps of
elementary contrasts
a
r
c
h
i
e
m
o
t
io
n
a
l
a
r
c
h
i
s
o
c
ia
l
a
r
c
h
i
s
p
a
t
ia
l
a
r
c
h
i
s
t
a
n
d
a
r
d
h
c
p
e
m
o
t
io
n
h
c
p
g
a
m
b
li
n
g
h
c
p
la
n
g
u
a
g
e
h
c
p
m
o
t
o
r
h
c
p
r
e
la
t
io
n
a
l
h
c
p
s
o
c
ia
l
h
c
p
w
m
r
s
v
p
la
n
g
u
a
g
e
archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Similarity between
cognitive description
of elementary contrasts
a
r
c
h
i
e
m
o
t
io
n
a
l
a
r
c
h
i
s
o
c
ia
l
a
r
c
h
i
s
p
a
t
ia
l
a
r
c
h
i
s
t
a
n
d
a
r
d
h
c
p
e
m
o
t
io
n
h
c
p
g
a
m
b
li
n
g
h
c
p
la
n
g
u
a
g
e
h
c
p
m
o
t
o
r
h
c
p
r
e
la
t
io
n
a
l
h
c
p
s
o
c
ia
l
h
c
p
w
m
r
s
v
p
la
n
g
u
a
g
e
archi emotional
archi social
archi spatial
archi standard
hcp emotion
hcp gambling
hcp language
hcp motor
hcp relational
hcp social
hcp wm
rsvp language 0
1
Pinho, A.L. et al. SciData(2018)
Pinho, A.L. et al. SciData(2020)
Second release:
Mental Time Travel battery
Gauthier, B., & van Wassenhove, V. (2016a,b)
Preference battery
Lebreton, M. et al. (2015)
ToM + Pain Matrices battery
Dodell-Feder, D. et al. (2010)
Jacoby, N. et al. (2015)
Richardson, H. et al. (2018)
Visual Short-Term Memory + Enumeration
tasks
Knops, A. et al. (2014)
Self-Reference Effect task
Genon, S. et al. (2014)
“Bang!” task
Campbell, K. L. et al. (2015)
First + Second releases:
All contrasts: 279
Elementary contrasts: 127
Spearman correlation
First Release: 0.21 (p ≤ 10−17)
Second Release: 0.21 (p ≤ 10−13)
First+Second Releases: 0.23 (p ≤ 10−72)
13/ 35
34. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Individual Functional Atlasing
35. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Variability of Functional Signatures
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
Individual z-maps
15/ 35
36. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Variability of Functional Signatures
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
0.00 0.25 0.50
read sentence vs. listen to sentence
read sentence vs. checkerboard
left hand vs. right hand
horizontal checkerboard vs. vertical checkerboard
mental subtraction vs. sentence
saccade vs. fixation
guess which hand vs. hand palm or back
object grasping vs. mimic orientation
mental motion vs. random motion
false-belief story vs. mechanistic story
false-belief tale vs. mechanistic tale
expression intention vs. expression gender
face trusty vs. face gender
face image vs. shape outline
punishment vs. reward
0.00 0.25 0.50
tongue vs. any motion
right foot vs. any motion
left foot vs. any motion
right hand vs. any motion
left hand vs. any motion
tale vs. mental addition
relational processing vs. visual matching
mental motion vs. random motion
tool image vs. any image
place image vs. any image
face image vs. any image
body image vs. any image
2-back vs. 0-back
read pseudowords vs. consonant strings
read words vs. consonant strings
read words vs. read pseudowords
read sentence vs. read jabberwocky
read sentence vs. read words
inter-subject correlation
intra-subject correlation
Intra- and inter- subject correlation of brain maps
15/ 35
37. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Study 1
Dictionary of cognitive components
38. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Dictionary of cognitive components
Decomposition of 51 contrasts
with dictionary learning
Individual topographies of
20 components (n = 13)
Each component gets the name
of the active condition from the
contrast with the highest value in
the dictionary.
Multi-subject, sparse dictionary learning:
min(Us )s=1...n,V∈C
n
X
s=1
∥Xs
− Us
V∥2
+ λ∥Us
∥1
,
with Xs
p×c , Us
p×k and Vk×c
Functional correspondence: dictionary
of functional profiles (V) common to
all subjects
Sparsity: ℓ1−norm penalty and
Us ≥ 0 , ∀s ∈ [n]
17/ 35
39. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Dictionary of cognitive components
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
Components are consistently mapped across subjects.
17/ 35
40. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Dictionary of cognitive components
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
Components are consistently mapped across subjects.
17/ 35
41. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Dictionary of cognitive components
Pinho, A.L. et al. Hum Brain Mapp(2021) n = 13
0.25 0.30 0.35 0.40 0.45 0.50 0.55
Intra-subject
correlation
Inter-subject
correlation
Correlations of the dictionary components on split-half data
Variability of topographies linked to individual differences.
17/ 35
42. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Study 2
Reconstruction of functional contrasts
43. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Reconstruction of functional contrasts
Leave-p-out CV (p=3 subjects)
experiment to learn the shared
representations from contrasts of
eleven tasks. (n = 13)
Predict all contrasts from the
remaining task
19/ 35
44. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Reconstruction of functional contrasts
Leave-p-out CV (p=3 subjects)
experiment to learn the shared
representations from contrasts of
eleven tasks. (n = 13)
Predict all contrasts from the
remaining task
Train a Ridge-regression model with individual
contrast maps i of tasks −j to predict task j on
individual contrast-maps s ̸= i:
b
ws,λ,j
= argminw∈Rc−1
X
i̸=s
∥Xi
j − Xi
−j w∥2
+ λ∥w∥2
Prediction output for one contrast of task j in
subject s:
b
Xs
j = Xs
−j b
ws,λ,j
.
Cross-validated R-squared for task j at location i:
R2
i (j) = 1 − means∈[n]
∥b
Xs
i,j − Xs
i,j ∥2
∥Xs
i,j ∥2
19/ 35
45. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Reconstruction of functional contrasts
Pinho, A.L. et al. Hum Brain Mapp(2021)
n = 13
max R2
Most of the brain regions
are covered by the
predicted functional
signatures.
19/ 35
46. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Reconstruction of functional contrasts
n = 13
Pinho, A.L. et al. Hum Brain Mapp(2021)
Ridge-Regression model
for the scrambled case:
b
ws,λ,j
= argminw∈Rc−1
X
i,k ̸= s
∥Xi
j −Xk
−j w∥2
+λ∥w∥2
Cross-validated R-squared:
R2
i (j) = 1 − means∈[n]
∥b
Xs
i,j − Xs′
i,j ∥2
∥Xs′
i,j ∥2
Permutations of subjects
decrease the proportion of
well-predicted voxels in all
tasks, showing that
topographies are driven by
subject-specific variability.
19/ 35
47. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Study 3
Example: Functional mapping of the language network
48. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Ex: Functional mapping of the language network
Goal: Cognitive profile of ROIs based on IBC language-related contrasts
Select ROIs / Select IBC contrasts
Individualize ROIs using dual-regression
and the left-out contrasts
R(s) = R pinv X(s)
X(s)
Voxelwise z-scores average for each
ROI at every selected contrast Pinho, A.L. et al. Hum Brain Mapp(2021)
21/ 35
49. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Ex: Functional mapping of the language network
Linear SVC (upper triangle)
Dummy Classifier (lower triangle)
LOGOCV scheme
Prediction within pairs of ROIs
13 groups = 13 participants
Pinho, A.L. et al. Hum Brain Mapp(2021) 21/ 35
50. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Conclusions
Functional atlasing using a large dataset in the task dimension
Investigation of common functional profiles between tasks
Common
functional profiles
Shared
behavioral responses
Mental
functions
22/ 35
51. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Conclusions
Functional atlasing using a large dataset in the task dimension
Investigation of common functional profiles between tasks
Common
functional profiles
Shared
behavioral responses
Mental
functions
Individual brain modeling using data with higher spatial resolution
generalize across subjects
elicit variability between subjects
22/ 35
52. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
FastSRM encoding analysis of naturalistic
stimuli
53. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Naturalistic Tasks from the third release
Clips task: 4 fMRI Sessions / 21 Runs
Nishimoto, S. et al. (2011)
Raiders task: 2 fMRI Sessions: 13 Runs
Haxby, J. V. et al. (2011)
24/ 35
54. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Analyzing naturalistic-stimuli fMRI data with FastSRM
Shared Response Model by Chen et al. (2015)
Fast Shared Response Model (FastSRM) by Richard et al. (2019):
https://hugorichard.github.io/FastSRM/
25/ 35
55. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Analyzing naturalistic-stimuli fMRI data with FastSRM
Shared Response Model by Chen et al. (2015)
Fast Shared Response Model (FastSRM) by Richard et al. (2019):
https://hugorichard.github.io/FastSRM/
Why FastSRM?
25/ 35
56. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Analyzing naturalistic-stimuli fMRI data with FastSRM
Shared Response Model by Chen et al. (2015)
Fast Shared Response Model (FastSRM) by Richard et al. (2019):
https://hugorichard.github.io/FastSRM/
Why FastSRM?
Standard GLM applied to naturalistic stimuli leads to high-dimensional
controlled-design models.
25/ 35
57. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Analyzing naturalistic-stimuli fMRI data with FastSRM
Shared Response Model by Chen et al. (2015)
Fast Shared Response Model (FastSRM) by Richard et al. (2019):
https://hugorichard.github.io/FastSRM/
Why FastSRM?
Standard GLM applied to naturalistic stimuli leads to high-dimensional
controlled-design models.
Unsupervised data-driven approach where the design matrix and the spatial maps
are learnt jointly is more wieldy.
25/ 35
58. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Analyzing naturalistic-stimuli fMRI data with FastSRM
Shared Response Model by Chen et al. (2015)
Fast Shared Response Model (FastSRM) by Richard et al. (2019):
https://hugorichard.github.io/FastSRM/
Why FastSRM?
Standard GLM applied to naturalistic stimuli leads to high-dimensional
controlled-design models.
Unsupervised data-driven approach where the design matrix and the spatial maps
are learnt jointly is more wieldy.
High-dimensional data (many voxels) require a decomposition method with
scalability.
25/ 35
59. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Qualitative description of FastSRM
Richard et al. (2019)
26/ 35
60. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Formal description of SRM
For all subjects and time frames, SRM can be formally defined as follows:
X = SW + E (1)
X ∈ RG×nv → concatenation of G brain images with v vertices for n=12 subjects
S ∈ RG×k → shared response: concatenation of the weights across time frames
W ∈ Rk×nv → concatenation of the k spatial components with v vertices for the n subjects
E → the additive noise
27/ 35
61. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Formal description of SRM
For all subjects and time frames, SRM can be formally defined as follows:
X = SW + E (1)
To estimate W and S, consider the following two-step SRM minimization problem:
∀s ∈ {1, ..., n}, argmin{Ws : Ws W T
s =Ik }
n
X
j=1
∥Xj − SWj∥2
= UsVs (2)
argminS
n
X
s=1
∥Xs − SWs∥2
=
1
n
n
X
s=1
XsWT
s , (3)
27/ 35
62. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
FastSRM algorithm
Reduce data: Dimensions of the input data are reduced using Principal Component
Analysis (PCA) on X, using c components, with c ≪ v, in order to estimate the
reduced data b
X, such that b
X ∈ RG×nc.
Apply SRM algorithm: b
X is applied on the two-step algorithm using alternate
minimization, in order to find both the shared response b
S and the spatial
components c
W in the reduced space.
Recover Spatial Components: The spatial components of each subject are recovered by
orthonormal regression using the shared response in reduced space b
S and the data
X.
UsDsVs = SVD(ST
Xs)
28/ 35
63. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Co-Smoothing, Double K-Fold CV for FastSRM for each task
1: b
X = PCA(X)
2: b
W,b
S = SRM(b
X)
3: UDV
(−R/2)
−4s
= SVD b
SX
(−R/2)
−4s
, where W
(−R/2)
−4s = UV
(−R/2)
−4s
4: S
(−R/2)
−4s =
Pn
z=1,z̸∈[4s]
X
(−R/2)
−4s
W
(−R/2)T
−4s
n−4
5: S
(R/2)
−4s =
Pn
z=1,z̸∈[4s]
X
(R/2)
−4s
W
(−R/2)T
−4s
n−4
6: UDV
(−R/2)
4s
= SVD S
(−R/2)
−4s X
(−R/2)
4s
, where W
(−R/2)
4s = UV
(−R/2)
4s
7: e
X
(R/2)
4s = S
(R/2)
−4s W
(−R/2)
4s
8: ρ
(s,r)
e
XX
=
X
e
X
(r)
s − µ
e
X
X
(r)
s − µX
qX
e
X
(r)
s − µ
e
X
2
X
X
(r)
s − µX
2
,
where µ
e
X
and µX represent the means of e
X
(r)
s and X
(r)
s , respectively.
CV scheme applied for each
task with K = 3 for 12
subjects and K = 2 for R runs
Co-Smoothing described in Wu, A. et al.(2018) NeurIPS
29/ 35
64. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Significance of group-level correlation of original vs.
reconstructed data from FastSRM
30/ 35
65. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Group-level activation between Raiders and Clips
q ⩽ 0.05
Top 10 regions of Glasser atlas w/ areas displaying
≥ 5% of significant voxels in both hemispheres
1 Auditory Association Cortex
2 Temporo-Parieto-Occipital Junction
3 Posterior Cingulate Cortex
4 Superior Parietal Cortex
5 Inferior Parietal Cortex
6 Early Auditory Cortex
7 Dorsal Stream Visual Cortex
8 Lateral Temporal Cortex
9 MT+Complex and Neighboring Visual Areas
10 Primary Visual Cortex (V1)
31/ 35
66. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Conclusions
IBC Third-Release reflect responses to behavior.
FastSRM is a computational low-cost approach to analyze task-fMRI time-series.
Useful to analyse high-dimensional paradigms, such as naturalistic stimuli
32/ 35
67. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Thanks!
Bertrand Thirion
The IBC volunteers!
33/ 35
68. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Concluding Remarks
Deep Behavioral Phenotyping in functional neuroimaging to study
cognitive-specific mechanisms
Remove idiosyncrasies of single tasks from the study
Map commonalities of different but similar tasks to assess consistency (ex: map
across different sensory modalities)
Isolate executive and perceptual mechanisms from the same high-order cognitive
function
Improve connectivity models
34/ 35
69. Summary Overview of IBC Data-quality assessment Individual Functional Atlasing FastSRM encoding experiment Acknowledgments and Remarks
Thank you for your attention.