Direct Estimation of Evoked Hemoglobin Changes by Multimodality Fusion Imaging

In the last two decades, both diffuse optical tomography (DOT) and blood oxygen level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) methods have been developed as noninvasive tools for imaging evoked cerebral hemodynamic changes in studies of brain activity. Although these two technologies measure functional contrast from similar physiological sources, i.e., changes in hemoglobin levels, these two modalities are based on distinct physical and biophysical principles leading to both limitations and strengths to each method. In this work, we describe a unified linear model to combine the complimentary spatial, temporal, and spectroscopic resolutions of concurrently measured optical tomography and fMRI signals. Using numerical simulations, we demonstrate that concurrent optical and BOLD measurements can be used to create cross-calibrated estimates of absolute micromolar deoxyhemoglobin changes. We apply this new analysis tool to experimental data acquired simultaneously with both DOT and BOLD imaging during a motor task, demonstrate the ability to more robustly estimate hemoglobin changes in comparison to DOT alone, and show how this approach can provide cross-calibrated estimates of hemoglobin changes. Using this multimodal method, we estimate the calibration of the 3tesla BOLD signal to be −0.55%±0.40% signal change per micromolar change of deoxyhemoglobin

Neural correlates of early deliberate emotion regulation: Young children\u27s responses to interpersonal scaffolding.

Deliberate emotion regulation, the ability to willfully modulate emotional experiences, is shaped through interpersonal scaffolding and forecasts later functioning in multiple domains. However, nascent deliberate emotion regulation in early childhood is poorly understood due to a paucity of studies that simulate interpersonal scaffolding of this skill and measure its occurrence in multiple modalities. Our goal was to identify neural and behavioral components of early deliberate emotion regulation to identify patterns of competent and deficient responses. A novel probe was developed to assess deliberate emotion regulation in young children. Sixty children (age 4-6 years) were randomly assigned to deliberate emotion regulation or control conditions. Children completed a frustration task while lateral prefrontal cortex (LPFC) activation was recorded via functional near-infrared spectroscopy (fNIRS). Facial expressions were video recorded and children self-rated their emotions. Parents rated their child\u27s temperamental emotion regulation. Deliberate emotion regulation interpersonal scaffolding predicted a significant increase in frustration-related LPFC activation not seen in controls. Better temperamental emotion regulation predicted larger LPFC activation increases post- scaffolding among children who engaged in deliberate emotion regulation interpersonal scaffolding. A capacity to increase LPFC activation in response to interpersonal scaffolding may be a crucial neural correlate of early deliberate emotion regulation

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory

There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Brain lateralization in children with upper‑limb reduction deficiency

Background: The purpose of the current study was to determine the influence of upper-limb prostheses on brain activity and gross dexterity in children with congenital unilateral upper-limb reduction deficiencies (ULD) compared to typically developing children (TD). Methods: Five children with ULD (3 boys, 2 girls, 8.76 ± 3.37 years of age) and five age- and sex-matched TD children (3 boys, 2 girls, 8.96 ± 3.23 years of age) performed a gross manual dexterity task (Box and Block Test) while measuring brain activity (functional near-infrared spectroscopy; fNIRS). Results: There were no significant differences (p = 0.948) in gross dexterity performance between the ULD group with prosthesis (7.23 ± 3.37 blocks per minute) and TD group with the prosthetic simulator (7.63 ± 5.61 blocks per minute). However, there was a significant (p = 0.001) difference in Laterality Index (LI) between the ULD group with prosthesis (LI = − 0.2888 ± 0.0205) and TD group with simulator (LI = 0.0504 ± 0.0296) showing in a significant ipsilateral control for the ULD group. Thus, the major finding of the present investigation was that children with ULD, unlike the control group, showed significant activation in the ipsilateral motor cortex on the non-preferred side using a prosthesis during a gross manual dexterity task. Conclusions: This ipsilateral response may be a compensation strategy in which the existing cortical representations of the non-affected (preferred) side are been used by the affected (non-preferred) side to operate the prosthesis. This study is the first to report altered lateralization in children with ULD while using a prosthesis. Trial registration The clinical trial (ClinicalTrial.gov ID: NCT04110730 and unique protocol ID: IRB # 614-16-FB) was registered on October 1, 2019 (https ://clini caltr ials.gov/ct2/show/NCT04 11073 0) and posted on October 1, 2019. The study start date was January 10, 2020. The first participant was enrolled on January 14, 2020, and the trial is scheduled to be completed by August 23, 2023. The trial was updated January 18, 2020 and is currently recruitin

Putting our heads together: interpersonal neural synchronization as a biological mechanism for shared intentionality

Shared intentionality, or collaborative interactions in which individuals have a shared goal and must coordinate their efforts, is a core component of human interaction. However, the biological bases of shared intentionality and, specifically, the processes by which the brain adjusts to the sharing of common goals, remain largely unknown. Using functional near infrared spectroscopy (fNIRS), coordination of cerebral hemodynamic activation was found in subject pairs when completing a puzzle together in contrast to a condition in which subjects completed identical but individual puzzles (same intention without shared intentionality). Interpersonal neural coordination was also greater when completing a puzzle together compared to two control conditions including the observation of another pair completing the same puzzle task or watching a movie with a partner (shared experience). Further, permutation testing revealed that the time course of neural activation of one subject predicted that of their partner, but not that of others completing the identical puzzle in different partner sets. Results indicate unique brain-to-brain coupling specific to shared intentionality beyond what has been previously found by investigating the fundamentals of social exchange

Validating an image-based fNIRS approach with fMRI and a working memory task

In the current study, we extend a previous methodological pipeline by adding a novel image reconstruction approach to move functional near-infrared (fNIRS) signals from channel-space on the surface of the head to voxel-space within the brain volume. We validate this methodology by comparing voxel-wise fNIRS results to functional magnetic resonance imaging (fMRI) results from a visual working memory (VWM) task using two approaches. In the first approach, significant voxel-wise correlations were observed between fNIRS and fMRI measures for all experimental conditions across brain regions in the fronto-parieto-temporal cortices. In the second approach, we conducted separate multi-factorial ANOVAs on fNIRS and fMRI measures and then examined the correspondence between main and interaction effects within common regions of interest. Both fMRI and fNIRS showed similar trends in activation within the VWM network when the number of items held in working memory increases. These results validate the image-based fNIRS approach

Functional imaging of cognition in an old-old population: A case for portable functional near-infrared spectroscopy

In this study, functional near-infrared spectroscopy (fNIRS) was used to record brain activa- tion during cognitive testing in older individuals (88±6yo; N = 19) living in residential care communities. This population, which is often associated with loss of personal independence due to physical or cognitive decline associated with aging, is also often under-represented in neuroscience research because of a limited means to participate in studies which often take place in large urban or university centers. In this study, we demonstrate the feasibility and initial results using a portable 8-source by 4-detector fNIRS system to measure brain activity from participants within residential care community centers. Using fNIRS, brain sig- nals were recorded during a series of computerized cognitive tests, including a Symbol Digit Coding test (SDC), Stroop Test (ST), and Shifting Attention Test (SAT). The SDC and SAT elicited greater activity in the left middle frontal region of interest. Three components of the ST produced increases in the right middle frontal and superior frontal, and left superior frontal regions. An association between advanced age and increased activation in the right middle frontal region was observed during the incongruent ST. Although none of the partici- pants had clinical dementia based on the short portable mental status questionnaire, the group performance was slightly below age-normed values on these cognitive tests. These results demonstrate the capability for obtaining functional neuroimaging measures in resi- dential settings, which ultimately may aid in prognosis and care related to dementia in older adults

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data

The NIRS Brain AnalyzIR Toolbox

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