Collegiate athlete brain data for white matter mapping and network neuroscience

Caron, Bradley; Stuck, Ricardo; McPherson, Brent; Bullock, Daniel; Kitchell, Lindsey; Faskowitz, Joshua; Kellar, Derek; Cheng, Hu; Newman, Sharlene D.; Port, Nicholas; Pestilli, Franco

doi:10.1038/s41597-021-00823-z

Cited by 5 publications

(3 citation statements)

References 98 publications

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“…We chose the Destrieux (aparc.a20090s) atlas for subsequent segmentation of white matter and mapping of diffusion metrics to the relevant cortical and subcortical structures. Supplementary and further processing of the anonymized and deidentified images were performed using processing pipelines via the brainlife.io secure cloud processing platform, as per methodology described by Caron et al (2021). In brief, following manual inspection of the images, additional FSL and Freesurfer steps via their equivalent brainlife apps were repeated as necessary to achieve satisfactory brain segmentation.…”

Section: Methodsmentioning

confidence: 99%

Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease

Lock,

Tan,

Long

et al. 2024

Front. Artif. Intell.

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Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias—ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term “AI ethics.” Further work is required to expand the approach proposed here into a reproducible strategy.

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Section: Methodsmentioning

confidence: 99%

Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease

Lock,

Tan,

Long

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Supplementary processing of the deidentified images was performed using processing pipelines via the brainlife.io secure cloud processing platform, as per methodology described by Caron et al (2021) . In brief, following manual inspection of the images, additional FSL and Freesurfer steps via their equivalent brainlife apps were repeated as necessary to achieve satisfactory brain segmentation.…”

Section: Methodsmentioning

confidence: 99%

Structural volumetric and Periodic Table DTI patterns in Complex Normal Pressure Hydrocephalus—Toward the principles of a translational taxonomy

Lock,

Toh,

Keong

2024

Front. Hum. Neurosci.

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IntroductionWe previously proposed a novel taxonomic framework to describe the diffusion tensor imaging (DTI) profiles of white matter tracts by their diffusivity and neural properties. We have shown the relevance of this strategy toward interpreting brain tissue signatures in Classic Normal Pressure Hydrocephalus vs. comparator cohorts of mild traumatic brain injury and Alzheimer’s disease. In this iteration of the Periodic Table of DTI Elements, we examined patterns of tissue distortion in Complex NPH (CoNPH) and validated the methodology against an open-access dataset of healthy subjects, to expand its accessibility to a larger community.MethodsDTI measures for 12 patients with CoNPH with multiple comorbidities and 45 cognitively normal controls from the ADNI database were derived using the image processing pipeline on the brainlife.io open cloud computing platform. Using the Periodic Table algorithm, DTI profiles for CoNPH vs. controls were mapped according to injury patterns.ResultsStructural volumes in most structures tested were significantly lower and the lateral ventricles higher in CoNPH vs. controls. In CoNPH, significantly lower fractional anisotropy (FA) and higher mean, axial, and radial diffusivities (MD, L1, and L2 and 3, respectively) were observed in white matter related to the lateral ventricles. Most diffusivity measures across supratentorial and infratentorial structures were significantly higher in CoNPH, with the largest differences in the cerebellum cortex. In subcortical deep gray matter structures, CoNPH and controls differed most significantly in the hippocampus, with the CoNPH group having a significantly lower FA and higher MD, L1, and L2 and 3. Cerebral and cerebellar white matter demonstrated more potential reversibility of injury compared to cerebral and cerebellar cortices.DiscussionThe findings of widespread and significant reductions in subcortical deep gray matter structures, in comparison to healthy controls, support the hypothesis that Complex NPH cohorts retain imaging features associated with Classic NPH. The use of the algorithm of the Periodic Table allowed for greater consistency in the interpretation of DTI results by focusing on patterns of injury rather than an over-reliance on the interrogation of individual measures by statistical significance alone. Our aim is to provide a prototype that could be refined for an approach toward the concept of a “translational taxonomy.”

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“…However, the relative brain and behavior signatures of this potentially enhanced ability is not well studied. There are several brain imaging datasets of college athletes 15 , amateur chess 16 or soccer players 17 available publicly, but the datasets on concentration cognitive task and their brain activity datasets for professional athletes, especially national master level athletes of Shooting, Archery and Modern Pentathlon are lacking.…”

Section: Background and Summarymentioning

confidence: 99%

A simultaneous electroencephalography and eye-tracking dataset in elite athletes during alertness and concentration tasks

Pei

Zhou

et al. 2022

Sci Data

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The dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings was collected from 31 professional athletes and 43 college students during alertness behavior task (ABT) and concentration cognitive task (CCT). The CCT experiment lasting 1–2 hours included five sessions for groups of the Shooting, Archery and Modern Pentathlon elite athletes and the controls. Concentration targets included shooting target and combination target with or without 24 different directions of visual distractors and 2 types of music distractors. Meditation and Schulte Grid trainings were done as interventions. Analysis of the dataset aimed to extract effective biological markers of eye movement and EEG that can assess the concentration level of talented athletes compared with same-aged controls. Moreover, this dataset is useful for the research of related visual brain-computer interfaces.

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Collegiate athlete brain data for white matter mapping and network neuroscience

Cited by 5 publications

References 98 publications

Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease

Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease

Structural volumetric and Periodic Table DTI patterns in Complex Normal Pressure Hydrocephalus—Toward the principles of a translational taxonomy

A simultaneous electroencephalography and eye-tracking dataset in elite athletes during alertness and concentration tasks

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