Evaluating the replicability, specificity, and generalizability of connectome fingerprints

Waller, Lea; Walter, Henrik; Kruschwitz, Johann; Reuter, Lucia; Müller, Sabine; Erk, Susanne; Veer, Ilya M.

doi:10.1016/j.neuroimage.2017.07.016

Cited by 64 publications

(87 citation statements)

References 24 publications

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“…Similar to previous results (Finn et al, 2015;Waller et al, 2017), we found lower identification accuracy in a longitudinal test sample with more "standard" MRI parameters (e.g., longer TR, shorter length of scan, fewer number of head coils, Figure 4B, pink ROC curve).…”

Section: Predictive Edges From One Sample Improves Accuracy In Indepesupporting

confidence: 90%

“…Previous methods, for instance, presume there is a "match" for each respective scan in the data set (i.e., each individual has at least two scans in the pool of available data) and do not consider false positives. Furthermore, given that research shows that identifiability of individuals decreases as sample size increases (Waller et al, 2017), it is important to account for sample size in the model. Additionally, while many studies show that the identification test metric for individual identifiability is significantly greater than would be expected by chance, it is difficult to know how meaningful this metric is when identification accuracy is in the range of ~40-60% (e.g., Horien et al, 2019).…”

Section: An Improved Statistical Framework For Identification Accuracymentioning

confidence: 99%

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Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5 years apart

Jalbrzikowski

Lei

Foran

et al. 2019

Preprint

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Initial studies found that individual correlational patterns from resting-state functional magnetic resonance imaging studies can accurately identify another scan from that same individual. This method is known as "connectotyping" or functional connectome "fingerprinting".We leveraged a unique dataset of 12-30 years old (N=140) individuals who had two distinct resting state scans on the same session (Visit 1, V1), and again 12-18 months later (Visit 2, V2; henceforth 1.5 years) to assess the sensitivity and specificity of identification accuracy across different time scales (same day, 1.5 years apart) and developmental periods (youths, adults).We also used multiple statistical methods to identify the connections that enhance fingerprinting accuracy. We found that sensitivity and specificity to identify one's own scan was high (overall average AUC: 0.94), and identifiability was significantly higher in the same session (average AUC: 0.97) in comparison to the 1.5-year comparison (average AUC:0.91). The level of fingerprinting accuracy in youths (average AUC:0.93) was not significantly different from adults (average AUC:0.96). Select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Finally, we found that identification of these features generalized across datasets and use of these features improve fingerprinting accuracy in an independent longitudinal data set (N=208). These results provide a framework for understanding that features of fingerprinting accuracy are stable from adolescence through adulthood. Importantly, these features contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in establishing one's connectome.

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Section: Predictive Edges From One Sample Improves Accuracy In Indepesupporting

confidence: 90%

Section: An Improved Statistical Framework For Identification Accuracymentioning

confidence: 99%

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5 years apart

Jalbrzikowski

Lei

Foran

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Recently, it has been demonstrated that the whole-brain pattern of resting-state functional connectivity (rsFC) can be used to identify an individual from a data pool with an accuracy of up to 99% (Anderson, Ferguson, Lopez-Larson, & Yurgelun-Todd, 2011;Finn et al, 2015;Miranda-Dominguez et al, 2014). This finding was further validated by studies with large sample sizes (Waller et al, 2017) and disease conditions (Kaufmann et al, 2017;Rosenberg et al, 2016). Thus, whole-brain rsFC patterns could be viewed as a connectome fingerprint containing an individual's identity features (Miranda-Dominguez et al, 2014).…”

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confidence: 87%

Classification of schizophrenia by intersubject correlation in functional connectome

Chen

Bai

et al. 2019

Human Brain Mapping

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Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting‐state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH–SCH) than different groups (i.e., HC–SCH). These findings suggest that the average connectome within group (termed as group‐specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75–77%) and area under curve (81–86%) were found in discriminating SCH from HC or depression patients by GFC‐based leave‐one‐out cross‐validation. Cross‐center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group‐specific biological features and may be helpful in clinical diagnosis for schizophrenia.

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“…In practice, often functional connectivity profiles, correlation matrices from resting state functional magnetic resonance imaging (rs-fMRI) data, are matched. Under such settings, identification accuracies as high as 94% for the Human Connectome Project (HCP) data or as high as 55% for data with more standard quality have been reported (Waller et al, 2017;Finn et al, 2015;Van Essen et al, 2013). The moniker fingerprinting comes from the idea of the fingerprint as a unique person-specific identifier.…”

Section: Introductionmentioning

confidence: 99%

On statistical tests of functional connectome fingerprinting

Wang

Sair

Crainiceanu

et al. 2018

Preprint

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Fingerprinting of functional connectomes is an increasingly standard measure of reproducibility in functional magnetic resonance imaging connectomics. In such studies, one attempts to match a subject's first session image with their second, in a blinded fashion, in a group of subjects measured twice.The number or percentage of correct matches is usually reported as a statistic. In this manuscript, we investigate the statistical tests of matching based on exchangeability assumption in the fingerprinting analysis. We show that a nearly universal Poisson(1) approximation applies for different matching schemes. We theoretically investigate the permutation tests and explore the issue that the test is overly sensitive to uninteresting directions in the alternative hypothesis, such as clustering due to familial status or demographics. We perform a numerical study on two functional magnetic resonance imaging (fMRI) resting state datasets, the Human Connectome Project (HCP) and the Baltimore Longitudinal Study of Aging (BLSA). These datasets are instructive, as the HCP includes techinical replications of long scans and includes monozygotic and dyzogotic twins as well as non-twin siblings. In contrast, the BLSA study incorporates more typical length resting state scans in a longitudinal study. Finally, a study of single regional connections is performed on the HCP data.

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Evaluating the replicability, specificity, and generalizability of connectome fingerprints

Cited by 64 publications

References 24 publications

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5 years apart

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5 years apart

Classification of schizophrenia by intersubject correlation in functional connectome

On statistical tests of functional connectome fingerprinting

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