Identifying individuals using fNIRS-based cortical connectomes

Rodrigues, Júlia de Souza; Ribeiro, Fernanda L.; Sato, João Ricardo; Mesquita, Rickson C.; Biazoli, Claudinei Eduardo

doi:10.1364/boe.10.002889

Cited by 24 publications

(12 citation statements)

References 28 publications

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“…Moreover, our results also suggested the apparent superiority of the GWB atlas, compared with the AAL atlas and the HBN atlas. Previous studies have indicated that the utilization of the GWB atlas has also resulted in satisfactory performances on other discriminative studies, which is consistent with our findings ( Valizadeh et al, 2018 ; de Souza Rodrigues et al, 2019 ). We speculated that this superiority may derive from the node number and the construction method of the GWB atlas.…”

Section: Discussionsupporting

confidence: 93%

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

et al. 2021

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Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

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Section: Discussionsupporting

confidence: 93%

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

et al. 2021

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“…Yet, with bigger and deeper data volumes, neuroscientists are confronted to a paradox: while big-data neuroscience approaches the realm of population neuroscience, we remain challenged by understanding how interindividual data variability echoes the singularity of the self (1, 3,8,9). This epistemological question has become particularly vivid with recent research showing that individuals can be identified from a cohort via their respective neural fingerprints derived from structural magnetic resonance imaging (MRI) (10,11), functional MRI (fMRI) (12)(13)(14)(15)(16), electroencephalography (EEG) (17)(18)(19), or functional near-infrared spectroscopy (fNIRS) (20). Strikingly, neural fingerprints are associated with individual traits such as global intelligence, working memory, and attention abilities (21)(22)(23)(24).…”

Section: Introductionmentioning

confidence: 99%

MEG, myself, and I: individual identification from neurophysiological brain activity

Castanheira

Orozco

Mišić

et al. 2021

Preprint

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Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. This variability challenges the sensitivity and specificity of analysis methods. Yet, recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully identify individuals within a cohort and predict their individual traits, yielding the notion of a neural fingerprint. Here, we aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of magnetoencephalography (MEG) resting-state brain activity in 158 participants. The resulting neurophysiological functional connectomes enabled the identification of individuals with similar identifiability rates to fMRI. We also show that individual identification was equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data indicate that identifiability can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: individuals remain identifiable from recordings performed weeks after their baseline reference data was collected. We can anticipate a vast range of diverse applications in personalized, clinical and basic neuroscience of individual differentiation from large-scale neural electrophysiology, in future longitudinal and cross-section studies.

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“…The functional organization of the adult brain has been extensively characterized via FC with a variety of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), electro-encephalography (EEG), magneto-encephalography (MEG), and electrocorticography (ECoG). FC patterns in the adult brain tend to be relatively stable within an individual, and similar patterns can be captured across subjects ( de Souza Rodrigues et al, 2019 ; Demuru and Fraschini, 2020 ; Finn et al, 2015 ; Gordon et al, 2017a , b ; Miranda-Dominguez et al, 2014 ). This within-individual stability in FC measures has been leveraged to understand individual differences or ‘trait’ variance by investigating neural patterns in single subjects and how they may relate to behavioral phenotypes ( Bosl et al, 2018 ; Finn and Todd Constable, 2016 ; Friedman et al, 2019 ; Gratton et al, 2016 ; Greene et al, 2018 ; Nostro et al, 2018 ; Oswald et al, 2017 ; Rosenberg et al, 2016 ; Seitzman et al, 2019 ; Yoo et al, 2018 ).…”

Section: Introductionmentioning

confidence: 94%

Predictive modeling of neurobehavioral state and trait variation across development

Sanchez-Alonso

Aslin

2020

Developmental Cognitive Neuroscience

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A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

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Identifying individuals using fNIRS-based cortical connectomes

Cited by 24 publications

References 28 publications

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

MEG, myself, and I: individual identification from neurophysiological brain activity

Predictive modeling of neurobehavioral state and trait variation across development

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