Macroanatomical Landmarks Featuring Junctions of Major Sulci and Fissures and Scalp Landmarks Based on the International 10-10 System for Analyzing Lateral Cortical Development of Infants

White matter microstructural integrity as a key to effective propagation of gamma entrainment in humans

Gamma entrainment through sensory stimulation has the potential to reduce the pathology of Alzheimer's disease in mouse models. However, clinical trials in Alzheimer's disease (AD) patients have yielded inconsistent results, necessitating further investigation. This single-center pre-post intervention study aims to explore the influence of white matter microstructural integrity on gamma rhythm propagation from the visual cortex to AD-affected regions in 31 cognitively normal volunteers aged ≥ 65. Gamma rhythm propagation induced by optimal FLS was measured. Diffusion tensor imaging was employed to assess the integrity of white matter tracts of interest. After excluding 5 participants with a deficit in steady-state visually evoked potentials, 26 participants were included in the final analysis. In the linear regression analyses, gamma entrainment was identified as a significant predictor of gamma propagation (p < 0.001). Furthermore, the study identified white matter microstructural integrity as a significant predictor of gamma propagation by flickering light stimulation (p < 0.05), which was specific to tracts that connect occipital and temporal or frontal regions. These findings indicate that, despite robust entrainment of gamma rhythms in the visual cortex, their propagation to other regions may be impaired if the microstructural integrity of the white matter tracts connecting the visual cortex to other areas is compromised. Consequently, our findings have expanded our understanding of the prerequisites for effective gamma entrainment and suggest that future clinical trials utilizing visual stimulation for gamma entrainment should consider white matter tract microstructural integrity for candidate selection and outcome analysis.

Fast and accurate computational E-field dosimetry for group-level transcranial magnetic stimulation targeting

Transcranial magnetic stimulation (TMS) is used to study brain function and treat mental health disorders. During TMS, a coil placed on the scalp induces an E-field in the brain that modulates its activity. TMS is known to stimulate regions that are exposed to a large E-field. Clinical TMS protocols prescribe a coil placement based on scalp landmarks. There are inter-individual variations in brain anatomy that result in variations in the TMS-induced E-field at the targeted region and its outcome. These variations across individuals could in principle be minimized by developing a large database of head subjects and determining scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. However, this approach requires repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements. We developed a probabilistic matrix decomposition-based approach for rapidly evaluating the E-field induced during TMS for a large number of coil placements due to a pre-defined coil model. Our approach can determine the E-field induced in over 1 Million coil placements in 9.5 h, in contrast, to over 5 years using a brute-force approach. After the initial set-up stage, the E-field can be predicted over the whole brain within 2-3 ms and to 2% accuracy. We tested our approach in over 200 subjects and achieved an error of <2% in most and <3.5% in all subjects. We will present several examples of bench-marking analysis for our tool in terms of accuracy and speed. Furthermore, we will show the methods' applicability for group-level optimization of coil placement for illustration purposes only. The software implementation link is provided in the appendix.

Fast And Accurate Population Level Transcranial Magnetic Stimulation via Low-Rank Probabilistic Matrix Decomposition (PMD)

Transcranial magnetic stimulation (TMS) is used to study brain function and treat mental health disorders. During TMS, a coil placed on the scalp induces an E-field in the brain that modulates its activity. TMS is known to stimulate regions that are exposed to a large E-field. Clinical TMS protocols prescribe a coil placement based on scalp landmarks. There are inter-individual variations in brain anatomy that result in variations in the TMS-induced E-field at the targeted region and its outcome. These variations across individuals could in principle be minimized by developing a large database of head subjects and determining scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. However, this approach requires repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements. We developed a probabilistic matrix decomposition-based approach for rapidly evaluating the E-field induced during TMS for a large number of coil placements. Our approach can determine the E-field induced in over 1 Million coil placements in 9.5 hours, in contrast, to over 5 years using a bruteforce approach. After the initial set-up stage, the E-field can be predicted over the whole brain within 2-3 milliseconds and to 2% accuracy. We tested our approach in over 200 subjects and achieved an error of < 2% in most and < 3.5% in all subjects. We will present several examples of bench-marking analysis for our tool in terms of accuracy and speed across and its applicability for population level optimization of coil placement.

Highlights

A method for practical E-field informed population-level TMS coil placement strategies is developed.This algorithm enables the determination of E-field informed optimal coil placement in seconds enabling it’s use for close-loop and on-the-fly reconfiguration of TMS.After the initial set-up stage of less than 10 hours, the E-field can be predicted for any coil placement across the whole brain within in 2-3 milliseconds.

Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood: a longitudinal MRI analysis

The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region-dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.

Methodological issues of the central mechanism of two classic acupuncture manipulations based on fNIRS: suggestions for a pilot study

Background: Acupuncture reinforcing-reducing manipulation (ARRM) is a necessary procedure of traditional Chinese acupuncture and an essential factor affecting the therapeutic effect of acupuncture. Shaoshanhuo reinforcing method (SSH) and Toutianliang reducing method (TTL) are the most representative ARRMs. They integrate six single ARRMs and pose distinguished therapeutic effects of acupuncture. However, due to the complexity, diversity, and variation, investigating the mechanism of these two classic manipulations is insufficient. The neuroimaging technique is an important method to explore the central mechanism of SSH and TTL. This study attempted to design a randomized crossover trial based on functional near-infrared spectroscopy (fNIRS) to explore the mechanism of SSH and TTL, meanwhile, provide valuable methodological references for future studies. Methods: A total of 30 healthy subjects were finally included and analyzed in this study. fNIRS examination was performed to record the neural responses during the two most representative ARRMs. The cortical activation and the inter-network functional connectivity (FC) were explored. Results: The results found that SSH and TTL could elicit significant cerebral responses, respectively, but there was no difference between them. Conclusion: Neuroimaging techniques with a higher spatiotemporal resolution, combinations of therapeutic effects, and strict quality control are important to neuroimaging studies on SSH and TTL.

Investigating developmental changes in scalp-to-cortex correspondence using diffuse optical tomography sensitivity in infancy

Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data require accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development. Aim: We aim to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years), and adults (20 to 24 years). Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs. Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs. Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate, realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.

Transcranial brain atlas for school-aged children and adolescents

BACKGROUND

Both fNIRS optodes and TMS coils are placed on the scalp, while the targeted brain activities are inside the brain. An accurate cranio-cortical correspondence is crucial to the precise localization of the cortical area under imaging or stimulation (i.e. transcranial locating), as well as guiding the placement of optodes/coils (i.e. transcranial targeting). However, the existing normative cranio-cortical correspondence data used as transcranial references are predominantly derived from the adult population, and whether and how correspondence changes during childhood and adolescence is currently unclear.

OBJECTIVE

This study aimed to build the age-specific cranio-cortical correspondences for school-aged children and adolescents and investigate its differences to adults.

METHODS

Age-specific transcranial brain atlases (TBAs) were built with age groups: 6-8, 8-10, 10-12, 12-14, 14-16, and 16-18 years. We compared the performance in both transcranial locating and targeting when using the age-appropriate TBA versus the adult TBA (derived from adult population) for children.

RESULTS

These atlases provide age-specific probabilistic cranio-cortical correspondence at a high resolution (average scalp spacing of 2.8 mm). Significant differences in cranio-cortical correspondence between children/adolescents and adults were found: the younger the child, the greater the differences. For children (aged 6-12 years), locating and targeting errors when using the adult TBA reached 10 mm or more in the bilateral temporal lobe and frontal lobe. In contrast, the age-matched TBA reduced these errors to 4-5 mm, an approximately 50% reduction in error.

CONCLUSION

Our work provides an accurate and effective anatomical reference for studies in children and adolescents.

Longitudinal infant fNIRS channel-space analyses are robust to variability parameters at the group-level: An image reconstruction investigation

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First investigation of validity of longitudinal infant channel-space fNIRS analysis.

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Novel image reconstruction analysis conducted.

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Variability in array position is dominant factor driving different inferences.

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Channel-space fNIRS analyses robust to implicit assumptions at group-level.

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Hope to encourage more widespread use of image reconstruction in infant analyses.

The first 1000 days from conception to two-years of age are a critical period in brain development, and there is an increasing drive for developing technologies to help advance our understanding of neurodevelopmental processes during this time. Functional near-infrared spectroscopy (fNIRS) has enabled longitudinal infant brain function to be studied in a multitude of settings. Conventional fNIRS analyses tend to occur in the channel-space, where data from equivalent channels across individuals are combined, which implicitly assumes that head size and source-detector positions (i.e. array position) on the scalp are constant across individuals. The validity of such assumptions in longitudinal infant fNIRS analyses, where head growth is most rapid, has not previously been investigated. We employed an image reconstruction approach to analyse fNIRS data collected from a longitudinal cohort of infants in The Gambia aged 5- to 12-months. This enabled us to investigate the effect of variability in both head size and array position on the anatomical and statistical inferences drawn from the data at both the group- and the individual-level. We also sought to investigate the impact of group size on inferences drawn from the data. We found that variability in array position was the driving factor between differing inferences drawn from the data at both the individual- and group-level, but its effect was weakened as group size increased towards the full cohort size (N = 53 at 5-months, N = 40 at 8-months and N = 45 at 12-months). We conclude that, at the group sizes in our dataset, group-level channel-space analysis of longitudinal infant fNIRS data is robust to assumptions about head size and array position given the variability in these parameters in our dataset. These findings support a more widespread use of image reconstruction techniques in longitudinal infant fNIRS studies.

Correlating functional near-infrared spectroscopy with underlying cortical regions of 0-, 1-, and 2-year-olds using theoretical light propagation analysis

Significance: The establishment of a light propagation analysis-based scalp-cortex correlation (SCC) between the scalp location of the source-detector (SD) pair and brain regions is essential for measuring functional brain development in the first 2 years of life using functional near-infrared spectroscopy (fNIRS). Aim: We aimed to reveal the optics-based SCC of 0-, 1-, and 2-year-olds (yo) and the suitable SD distance for this age period. Approach: Light propagation analyses using age-appropriate head models were conducted on SD pairs at 10-10 fiducial points on the scalp to obtain optics-based SCC and its metrics: the number of corresponding brain regions ( N C B R ), selectivity and sensitivity of the most likely corresponding brain region (MLCBR), and consistency of the MLCBR across developmental ages. Moreover, we assessed the suitable SD distances for 0-, 1-, and 2-yo by simultaneously considering the selectivity and sensitivity of the MLCBR. Results: Age-related changes in the SCC metrics were observed. For instance, the N C B R of 0-yo was larger than that of 1- and 2-yo. Conversely, the selectivity of 0-yo was lower than that of 1- and 2-yo. The sensitivity of 1-yo was higher than that of 0-yo at 15- to 30-mm SD distances and higher than that of 2-yo at 10-mm SD distance. Notably, the MLCBR of the fiducial points around the longitudinal fissure was inconsistent across age groups. An SD distance between 15 and 25 mm was found to be appropriate for satisfying both sensitivity and selectivity requirements. In addition, this work provides reference tables of optics-based SCC for 0-, 1-, and 2-yo. Conclusions: Optics-based SCC will be informative in designing and explaining child developmental studies using fNIRS. The suitable SD distances were between 15 and 25 mm for the first 2 years of life.

Characterizing the Action-Observation Network Through Functional Near-Infrared Spectroscopy: A Review

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that has undergone tremendous growth over the last decade due to methodological advantages over other measures of brain activation. The action-observation network (AON), a system of brain structures proposed to have “mirroring” abilities (e.g., active when an individual completes an action or when they observe another complete that action), has been studied in humans through neural measures such as fMRI and electroencephalogram (EEG); however, limitations of these methods are problematic for AON paradigms. For this reason, fNIRS is proposed as a solution to investigating the AON in humans. The present review article briefly summarizes previous neural findings in the AON and examines the state of AON research using fNIRS in adults. A total of 14 fNIRS articles are discussed, paying particular attention to methodological choices and considerations while summarizing the general findings to aid in developing better protocols to study the AON through fNIRS. Additionally, future directions of this work are discussed, specifically in relation to researching AON development and potential multimodal imaging applications.

Construction and validation of a database of head models for functional imaging of the neonatal brain

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.

Maternal speech shapes the cerebral frontotemporal network in neonates: A hemodynamic functional connectivity study

Language development and the capacity for communication in infants are predominantly supported by their mothers, beginning when infants are still in utero. Although a mother's speech should thus have a significant impact on her neonate's brain, neurocognitive evidence for this hypothesis remains elusive. The present study examined 37 neonates using near-infrared spectroscopy and observed the interactions between multiple cortical regions while neonates heard speech spoken by their mothers or by strangers. We analyzed the functional connectivity between regions whose response-activation patterns differed between the two types of speakers. We found that when hearing their mothers' speech, functional connectivity was enhanced in both the neonatal left and right frontotemporal networks. On the left it was enhanced between the inferior/middle frontal gyrus and the temporal cortex, while on the right it was enhanced between the frontal pole and temporal cortex. In particular, the frontal pole was more strongly connected to the left supramarginal area when hearing speech from mothers. These enhanced frontotemporal networks connect areas that are associated with language (left) and voice processing (right) at later stages of development. We suggest that these roles are initially fostered by maternal speech.

Array Designer: automated optimized array design for functional near-infrared spectroscopy

The position of each source and detector "optode" on the scalp, and their relative separations, determines the sensitivity of each functional near-infrared spectroscopy (fNIRS) channel to the underlying cortex. As a result, selecting appropriate scalp locations for the available sources and detectors is critical to every fNIRS experiment. At present, it is standard practice for the user to undertake this task manually; to select what they believe are the best locations on the scalp to place their optodes so as to sample a given cortical region-of-interest (ROI). This process is difficult, time-consuming, and highly subjective. Here, we propose a tool, Array Designer, that is able to automatically design optimized fNIRS arrays given a user-defined ROI and certain features of the available fNIRS device. Critically, the Array Designer methodology is generalizable and will be applicable to almost any subject population or fNIRS device. We describe and validate the algorithmic methodology that underpins Array Designer by running multiple simulations of array design problems in a realistic anatomical model. We believe that Array Designer has the potential to end the need for manual array design, and in doing so save researchers time, improve fNIRS data quality, and promote standardization across the field.