49 research outputs found
A Parameter-efficient Multi-subject Model for Predicting fMRI Activity
This is the Algonauts 2023 submission report for team "BlobGPT". Our model
consists of a multi-subject linear encoding head attached to a pretrained trunk
model. The multi-subject head consists of three components: (1) a shared
multi-layer feature projection, (2) shared plus subject-specific low-dimension
linear transformations, and (3) a shared PCA fMRI embedding. In this report, we
explain these components in more detail and present some experimental results.
Our code is available at https://github.com/cmi-dair/algonauts23
GREMLIN: Graph Estimation From MR Images Leading to Inference in Neuroscience
In recent years there has been a growing desire to understand the structure and function of the human brain. Approximately 1 in 5 adults suffers from mental illness, and many of these illnesses, including Alzheimer's Disease, Autism Spectrum Disorders, ADHD, and Schizophrenia could be described as connectopathies and may appear when observing the connectome (structural map of the brain). To this end, an abundance of MRI datasets have been collected around the globe. Of particular interest when seeking a connectome are the diffusion weighted (DTI) and structural (MPRAGE) sequences. Tools have been developed to process these brain images and enable quantitative analysis of brain structure. However, these tools often require computational expertise, and there exist few options to perform end-to-end analysis of MR images easily. Previous iterations of end-to-end connectome estimation pipelines have been limited in their ability to run at scale in parallel and have complex dependencies and setup routines.
We have developed a one-click open-source pipeline which allows for the reliable estimation of connectomes from MR data across multiple scales. The pipeline produced, ndmg, has been engineered to optimize the discriminability of resulting graphs across many datasets, effectively optimizing the lower bound of predictive accuracy for any downstream inference task. The ndmg pipeline has been used to generate connectomes from all known redistributable DTI and MPRAGE datasets to date, resulting in over 5,000 subjects processed and over 100,000 estimated connectomes across multiple scales. All of the connectomes we produced are made available through our graph database, MR-GRUTEDB. The code for this open-source pipeline is available at http://m2g.io. A web service, C4, also exists in which users can upload their MRI data and receive an estimated connectome in return at no cost. These tools lower the barrier for entry to connectomics by removing significant computational duress from researchers. This pipeline empowers reproducible science by abstracting hyper-parameter selection and over-fitting opportunities from researchers when processing their data, and enables mega-analysis of MR data across sites and studies, further opening the door for interesting and powerful scientific discovery
Numerical Stability of DeepGOPlus Inference
Convolutional neural networks (CNNs) are currently among the most widely-used
neural networks available and achieve state-of-the-art performance for many
problems. While originally applied to computer vision tasks, CNNs work well
with any data with a spatial relationship, besides images, and have been
applied to different fields. However, recent works have highlighted how CNNs,
like other deep learning models, are sensitive to noise injection which can
jeopardise their performance. This paper quantifies the numerical uncertainty
of the floating point arithmetic inaccuracies of the inference stage of
DeepGOPlus, a CNN that predicts protein function, in order to determine its
numerical stability. In addition, this paper investigates the possibility to
use reduced-precision floating point formats for DeepGOPlus inference to reduce
memory consumption and latency. This is achieved with Monte Carlo Arithmetic, a
technique that experimentally quantifies floating point operation errors and
VPREC, a tool that emulates results with customizable floating point precision
formats. Focus is placed on the inference stage as it is the main deliverable
of the DeepGOPlus model that will be used across environments and therefore
most likely be subjected to the most amount of noise. Furthermore, studies have
shown that the inference stage is the part of the model which is most disposed
to being scaled down in terms of reduced precision. All in all, it has been
found that the numerical uncertainty of the DeepGOPlus CNN is very low at its
current numerical precision format, but the model cannot currently be reduced
to a lower precision that might render it more lightweight.Comment: 11 pages, 5 figures, 2 table
Pipeline-Invariant Representation Learning for Neuroimaging
Deep learning has been widely applied in neuroimaging, including predicting
brain-phenotype relationships from magnetic resonance imaging (MRI) volumes.
MRI data usually requires extensive preprocessing prior to modeling, but
variation introduced by different MRI preprocessing pipelines may lead to
different scientific findings, even when using the identical data. Motivated by
the data-centric perspective, we first evaluate how preprocessing pipeline
selection can impact the downstream performance of a supervised learning model.
We next propose two pipeline-invariant representation learning methodologies,
MPSL and PXL, to improve robustness in classification performance and to
capture similar neural network representations. Using 2000 human subjects from
the UK Biobank dataset, we demonstrate that proposed models present unique and
shared advantages, in particular that MPSL can be used to improve out-of-sample
generalization to new pipelines, while PXL can be used to improve within-sample
prediction performance. Both MPSL and PXL can learn more similar
between-pipeline representations. These results suggest that our proposed
models can be applied to mitigate pipeline-related biases, and to improve
prediction robustness in brain-phenotype modeling.Comment: Extended Abstract presented at Machine Learning for Health (ML4H)
symposium 2022, November 28th, 2022, New Orleans, United States & Virtual,
http://www.ml4h.cc, 17 page
A numerical variability approach to results stability tests and its application to neuroimaging
Ensuring the long-term reproducibility of data analyses requires results
stability tests to verify that analysis results remain within acceptable
variation bounds despite inevitable software updates and hardware evolutions.
This paper introduces a numerical variability approach for results stability
tests, which determines acceptable variation bounds using random rounding of
floating-point calculations. By applying the resulting stability test to
\fmriprep, a widely-used neuroimaging tool, we show that the test is sensitive
enough to detect subtle updates in image processing methods while remaining
specific enough to accept numerical variations within a reference version of
the application. This result contributes to enhancing the reliability and
reproducibility of data analyses by providing a robust and flexible method for
stability testing
Neural correlates of polygenic risk score for autism spectrum disorders in general population
Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case-control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.
Keywords: autism spectrum disorders; cortical thickness; genetics; polygenic risk score; structural connectivity
Evaluating the Reliability of Human Brain White Matter Tractometry
Published Nov 17, 2021The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.This work was supported through grant 1RF1MH121868-
01 from the National Institute of Mental Health/the BRAIN
Initiative, through grant 5R01EB027585-02 to Eleftherios
Garyfallidis (Indiana University) from the National Institute
of Biomedical Imaging and Bioengineering, through Azure
Cloud Computing Credits for Research & Teaching provided
through the University of Washington’s Research
Computing unit and the University of Washington eScience
Institute, and NICHD R21HD092771 to Jason D. Yeatma