Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy

Santosa, Hendrik; Aarabi, Ardalan; Perlman, Susan B.; Huppert, Theodore J.

doi:10.1117/1.jbo.22.5.055002

Cited by 74 publications

(119 citation statements)

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“…It is not uncommon to observe a several-fold difference in the signal-to-noise ratio in measurements between areas with little hair (e.g., the forehead) and those with hair or thicker bone structure (e.g., the occipital). The use of statistical models whose assumptions do not match these properties often results in unacceptable false-discovery and uncontrolled type-I errors.As our group has reviewed in several recent publications [9,10,[15][16][17][18], these noise features and unique statistical properties of fNIRS data need to be properly considered and will be briefly summarized in this publication in the context of a new fNIRS analysis toolbox.The primary rationale for the development of the AnalyzIR (pronounced "an-a-lyze-er") toolbox was to create a statistical analysis package to specifically address the properties of fNIRS data. This toolbox was designed to capture and preserve as much of this fNIRS-specific information and noise as possible through the entire analysis pipeline such that first-and higher-level statistical analysis methods could use this information in statistical models by utilizing covariance whitening, accounting for dependent noise terms, and using robust statistical methods.…”

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The NIRS Brain AnalyzIR Toolbox

et al. 2018

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Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650-900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first-and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.Algorithms 2018, 11, 73 2 of 33 measurements are recorded from the scalp's surface, fNIRS is also more sensitive to contamination from superficial physiology in the skin, which poses unique challenges for data analysis [5,6].Over the last three decades, fNIRS has been used in a wide range of studies including pediatric populations (e.g., [7]), clinical studies (e.g., [8]), multimodal validations (e.g., [9]), and cognitive testing (e.g., [10]). However, as the fNIRS field has evolved and this technology has found more acceptance, the complexity of the scientific questions being asked of fNIRS data has dramatically increased. Group-level comparisons, longitudinal analysis, or complex comparisons between different task events are now status quo. In addition, fNIRS studies have been expanded to child and infant populations (reviewed in [7]), to allow a range of motion (including fNIRS studies of brain activity during gait or balance [11]), and more "real-world" experience (reviewed in [12]). However, these studies create challenges to analysis such as the need to deal with complex sources of motion and/or physiological noise artifacts. To date, the majority of fNIRS studies using tools and methods have either borrowed from other fields (primarily functional MRI) or have used modality-agnostic methods such as ordinary least-squares regression or methods coded in general programs such as statistical package for the social sciences (SPSS) [13] or statistical analysis system (SAS) [14]. However, in ge...

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“…The input of the connICA method are the functional connectivity matrices obtained for each participant, which were computed based on a robust Pearson’s correlation approach as recommended in Santosa et al, 2017. A high degree of similarity was observed at the individual and at the group level in the configuration of the functional connectivity matrices (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Brain imaging techniques are extremely sensitive to motion induced artifacts that are commonly observed in acquisitions on awake participants. Collecting RSFC data from awake infants considerably degrades the reliability of the inferred temporal correlations between voxel or channel time courses (Santosa et al, 2017). Because our goal was to collect high quality and reliable RSFC data, we decided to test participants during natural sleep only, which consequently also allowed us to perform longer recordings.…”

Section: Discussionmentioning

confidence: 99%

“…This robust correlation approach reduces the contribution of possible outlier time points (e.g. due to residual motion artefacts after preprocessing and wavelet denoising) in the correlation estimation (Santosa et al, 2017). Briefly, for each i , j element representing the preprocessed time series of channels i and j , a joint weighting matrix is calculated as …”

Section: Methodsmentioning

confidence: 99%

“…The correlation for each entry of the functional connectivity matrix is computed between the preprocessed weighted signals i W , j W . In this study, we defined k = 4.685 as this is the value usually employed in the literature, and we calculated σ from the median absolute deviation (MAD) of the signal r as σ = 1.4826 MAD ( r ) (Santosa et al, 2017). Individual robust functional connectivity matrices representing the temporal association between all of the channels were defined for HbO and HbR, where the i W , j W element reflects the robust Pearson’s correlation between channels i W and j W .…”

Section: Methodsmentioning

confidence: 99%

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Monolingual and bilingual infants rely on the same brain networks: Evidence from resting-state functional connectivity

Blanco

Molnar

Carreiras

et al. 2020

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15This study examines whether bilingual exposure has a profound effect on the functional organization of the 16 developing human brain during infancy. Recent behavioural research attests that monolingual vs. bilingual 17 experience affects cognitive and linguistic processes already during the first months of life. However, to what 18 extent the intrinsic organization of the infant human brain adapts to monolingual vs. bilingual environments is 19 unclear. We measured spontaneous hemodynamic brain activity using functional near-infrared spectroscopy 20 (fNIRS) in a large cohort (N=99) of 4-month-old monolingual and bilingual infants. We implemented well-21 established analysis approaches of functional brain imaging that enabled us to reveal the functional organization 22 of the infant brain in large-scale cortical networks, and to perform group-level comparisons (i.e., monolingual 23 vs. bilingual groups) in a reliable manner. Our results revealed no differences between the intrinsic functional 24 organization of the developing monolingual and bilingual infant brain at 4 months of age. 26Keywords: Functional connectivity, functional near-infrared spectroscopy, brain development, language 27 acquisition, bilingualism, independent component analysis, connectome . 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 85 profound effect on the developing human brain, such as its functional organization, is currently unknown. 86Measuring RSFC in monolingual and bilingual infants can reflect whether bilingual experience during the first 87 months of life leads to specific adaptations in the intrinsic properties of brain's function that are observable in 88 the absence of any task or stimuli (i.e., at rest). Testing whether functional adaptation to a bilingual context is 89 evident at the earliest stages of human development is crucial for our understanding of how bilingualism interacts 90 with general brain maturation patterns beyond task-specific language and cognitive processing. There is evidence 91 that long-term exposure to two languages might alter the brain's functional (Parker Jones et al., 2011; Berken 92 et al., 2016) and structural organization (Mechelli et al., 2004; García-Pentón et al., 2014; Mohades et al., 93 2015), as demonstrated by MRI studies in adults. For instance, stronger functional connectivity in bilingual 94 adults as compared to monolinguals has been observed in long-range bilateral and anterior-posterior connections 95 on both hemispheres (Luk et al., 2011), and in brain networks associated with language and executive control 96 processes (Grady et al., 2015; Berken et al., 2016). The aim of the current study is to assess whether the brain's 97 functional architecture begins to adapt to a bilingual environment as early as 4 months of age, by the time neural 98 and behavioural responses to external stimuli often differ across monolinguals and bilinguals. Specifically, the 99 current goal is to contrast the RSFC across monolingual and bilingual 4-month-old infants using fun...

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Dysfunction in interpersonal neural synchronization as a mechanism for social impairment in autism spectrum disorder

et al. 2021

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Social deficits in autism spectrum disorder (ASD) have been linked to atypical activation of the mentalizing network. This work, however, has been limited by a focus on the brain activity of a single person during computerized social tasks rather than exploring brain activity during in vivo interactions. The current study assessed neural synchronization during a conversation as a mechanism for social impairment in adults with ASD (n = 24) and matched controls (n = 26). Functional near‐infrared spectroscopy (fNIRS) data were collected from the prefrontal cortex (PFC) and tempoparietal junction (TPJ). Participants self‐reported on their social communication and videos of the interaction were coded for utterances and conversational turns. As expected, controls showed more neural synchrony than participants with ASD in the TPJ. Also as expected, controls showed less social communication impairment than participants with ASD. However, participants with ASD did not have fewer utterances compared with control subjects. Overall, less neural synchrony in the TPJ was associated with higher social impairment and marginally fewer utterances. Our findings advance our understanding of social difficulties in ASD by linking them to decreased neural synchronization of the TPJ. Lay Summary The coordination of brain responses is important for efficient social interactions. The current study explored the coordination of brain responses in neurotypical adults and adults with ASD to investigate if difficulties in social interactions are related to difficulties coordinating brain responses in ASD. We found that participants with ASD had more difficulties coordinating brain responses during a conversation with an interacting partner. Additionally, we found that the level of coordination in brain responses was linked to problems with social communication.

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Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy

Cited by 74 publications

References 75 publications

The NIRS Brain AnalyzIR Toolbox

The NIRS Brain AnalyzIR Toolbox

Monolingual and bilingual infants rely on the same brain networks: Evidence from resting-state functional connectivity

Dysfunction in interpersonal neural synchronization as a mechanism for social impairment in autism spectrum disorder

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