Biology subjects

Koba, C.

Publications and source records attributed to Koba, C..

7 recordsLinked to original sources

bioRxiv2025

Atypical functional connectome in congenitally blind humans

The human cortex is organized along continuous functional gradients that capture systematic transitions in functional connectivity across the brain. These gradients describe large-scale organizational principles, including hierarchical transitions from unimodal to transmodal regions. Here, we provide the first characterization of cortical gradients in a large sample of congenitally blind (n = 41) and sighted (n = 44) adults to assess the relative contributions of intrinsic (genetic) and experiential factors to cortical gradient organization. Using resting-state fMRI, we compared functional connectome gradients and their association with cortical structure. Both groups exhibited similar principal gradients: unimodal to transmodal, somatosensory to visual, and frontoparietal segregation, demonstrating that the fundamental scaffold of cortical organization emerges largely independently of visual experience. However, blindness altered specific features of the functional connectome: the visual network was more segregated from the sensorimotor network and more integrated with transmodal and frontoparietal networks. Moreover, blind individuals showed reduced canonical hierarchical ordering within early visual areas, weaker structure-function coupling in visual and temporal regions, and altered functional areal boundaries in V1. These findings suggest that the development of large-scale cortical gradients reflects a genetically guided scaffold that is subsequently refined by sensory experience.

neuroscience↗

bioRxiv2024

Structure-Function Relationship in Electrical and Hemodynamic Brain Networks: Insights from EEG and fNIRS during Rest and Task States

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, we characterize global and local structure-function coupling using source-reconstructed Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.

neuroscience↗

bioRxiv2024

Exploring the Impact of Ischemic Stroke on Brain Function: Insights from Functional Connectivity Gradients and Temporal Lag Correction

Ischemic brain stroke disrupts blood flow, leading to functional and structural changes associated with behavioral deficits. Importantly, despite this disruption occurring in localized regions, the resulting changes in the functional organization are both high-dimensional and widespread across the human cortex. However, the mechanisms with which these global patterns emerge and the subsequent behavioral deficits they entail, remain largely unexplored. Functional connectivity gradients provide consistent, reproducible, and robust low-dimensional representations of brain function that can be explored to reduce brain heterogeneity to a handful of axes along which brain function is organized. Here, we investigated how stroke disrupts this canonical gradient space by aligning each patient to a control-averaged gradient embedding and computing the distances to the "correct" positions to quantify functional deviations and their contribution to behavioral deficits. Importantly, we explicitly corrected these gradients for stroke-induced hemodynamic lags to further study their contribution. We found that lag correction enhanced the functional connectivity gradients most prominently in the second gradient, on which visual and somatomotor function is concentrated. Additionally, we identified significant functional deviations primarily within somatomotor, visual, and ventral attention networks, correlating with behavioral impairments. We studied the hemispheric asymmetries of these deviations finding that intact hemispheres preserve comparable patterns of asymmetry while damaged ones presented important changes. Lastly, right-sided lesions displayed more localized functional deviations than their contralateral lesions. Overall, we provide evidence that 1) correcting for hemodynamic lags improves gradient accuracy, as indicated by increased percentages of explained variance, and 2) behavioral impairments and hemispheric asymmetries result from a repositioning of region-based connectivity profiles in a low-dimensional, interpretable space. This suggests that large-scale brain function alterations manifest in slight, predictable movements largely confined to the visual-somatomotor axis.

neuroscience↗

bioRxiv2023

Investigating the Interaction Between EEG and fNIRS: A Multimodal Network Analysis of Brain Connectivity

The brain is a complex system with functional and structural networks. Different neuroimaging methods have their strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques has gained interest, but understanding how the information derived from these modalities is related remains an exciting open question. Successful integration of these modalities requires a sophisticated mathematical framework that goes beyond simple comparative analyses. The multilayer network model has emerged as a promising approach. This study is an extended version of the conference paper "Resting State Brain Connectivity Analysis from EEG and FNIRS Signals" [5]. In this study, we explored the brain network properties obtained from EEG and fNIRS data using graph analysis. Additionally, we adopted the multilayer network model to evaluate the benefits of combining multiple modalities compared to using a single modality. A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which is dependent on the modality and on the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.

neuroscience↗

bioRxiv2023

Resting State Brain Connectivity analysis from EEG and FNIRS signals

Contemporary neuroscience is highly focused on the synergistic use of machine learning and network analysis. Indeed, network neuroscience analysis intensively capitalizes on clustering metrics and statistical tools. In this context, the integrated analysis of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides complementary information about the electrical and hemodynamic activity of the brain. Evidence supports the mechanism of the neurovascular coupling mediates brain processing. However, it is not well understood how the specific patterns of neuronal activity are represented by these techniques. Here we have investigated the topological properties of functional networks of the resting-state brain between synchronous EEG and fNIRS connectomes, across frequency bands, using source space analysis, and through graph theoretical approaches. We observed that at global-level analysis small-world topology network features for both modalities. The edge-wise analysis pointed out increased inter-hemispheric connectivity for oxy-hemoglobin compared to EEG, with no differences across the frequency bands. Our results show that graph features extracted from fNIRS can reflect both shortand longrange organization of neural activity, and that is able to characterize the large-scale network in the resting state. Further development of integrated analyses of the two modalities is required to fully benefit from the added value of each modality. However, the present study highlights that multimodal source space analysis approaches can be adopted to study brain functioning in healthy resting states, thus serving as a foundation for future work during tasks and in pathology, with the possibility of obtaining novel comprehensive biomarkers for neurological diseases.

neuroscience↗

bioRxiv2023

An intact oculomotor neural circuit in congenital blindness

In the past three decades, multiple studies revealed that congenital blindness is associated with functional and structural reorganization in early visual areas and its interaction with other neural systems. Among the most reproducible findings is the weaker connectivity between the visual and sensorimotor cortices, which in sighted individuals plays a role in eye-motor coordination. Here we demonstrate an important exception to this reorganization phenomena: we find that in congenitally blind individuals (as for normally sighted ones), spontaneous, non-controlled eye movements correlate with connectivity between visual and sensorimotor cortices. Furthermore, using time-lagged regression, we show that eye movements drive activity in the visual cortex, which subsequently drives sensorimotor activity. Remarkably, this phenomenon persists even though blind participants often exhibit unsynchronized eye movements and cannot sense or regulate their eye positions. These findings provide evidence of a preserved, "non-functional" connectivity in congenitally blind individuals and reveal a firm, hard-wired constraint on connectivity that remains immune to any reorganization phenomena produced by lack of visual experience and oculomotor control.

neuroscience↗

bioRxiv2020

Spontaneous eye-movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity

During wakeful rest, individuals make small eye movements when asked to fixate. We examined how these endogenously-driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye-tracking (Nilsonne et al., 2016). The eye-tracking data indicated minor movements during rest, on the order of 1.0 degree on average when analyzed over 2sec epochs, which correlated modestly with RS BOLD data. However, the eye-tracking data correlated well with echo-planar imaging (EPI) time series sampled from the area of the Eye-Orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. We found that EO-EPI data correlated with activity in an extensive motor and sensory-motor network, but also some components of the dorsal attention network including the frontal and supplementary eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensory-motor and visual areas. For three different network sparsity levels, the resulting RS connectivity networks showed higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations, or as a function of different experimental conditions.

neuroscience↗