Biology subjects

Shikauchi, Y.

Publications and source records attributed to Shikauchi, Y..

3 recordsLinked to original sources

bioRxiv2025

Iron deposition and functional connectivity alterations in the right substantia nigra of adult males with autism

The substantia nigra (SN) is a midbrain nucleus implicated not only in motor control and reward processing but also in higher-order cognitive functions. Iron homeostasis in this region is essential for neurotransmitter synthesis, especially for dopamine, and thus, iron dysregulation may contribute to the symptomatology of autism spectrum disorder (ASD). However, iron deposition and functional circuits of the SN in the autistic brain remain underexplored. This study investigated iron deposition and functional connectivity (FC) of the SN in 53 adult males with ASD and 99 typically developing controls using quantitative susceptibility mapping and resting-state fMRI. Compared to controls, the ASD group exhibited higher magnetic susceptibility in the right SN, suggesting elevated iron deposition. Within the ASD group, higher iron deposition was associated with more severe socio-communicative deficits and reduced sensory-seeking behavior. Seed-based FC analyses further revealed that the ASD group exhibited stronger FC between the right SN and bilateral visual cortices and reduced FC with the right superior frontal gyrus. These results highlight the critical role of SN in the autistic brain and indicate that altered iron homeostasis in the SN may contribute to disruptions in the dopaminergic system that underlie the core symptoms of ASD.

neuroscience↗

bioRxiv2025

Quantifying state-dependent control properties of brain dynamics from perturbation responses

The brain can be conceptualized as a control system facilitating transitions between states, such as from rest to motor activity. Applying network control theory to measurements of brain signals enables characterization of brain dynamics through control properties. However, most prior studies that have applied network control theory have evaluated brain dynamics under unperturbed conditions, neglecting the critical role of external perturbations in accurate system identification. In this study, we combine a perturbation input paradigm with a network control theory framework and propose a novel method for estimating the controllability Gramian matrix in a simple, theoretically grounded manner. This method provides insights into brain dynamics, including overall controllability (quantified by the Gramians eigenvalues) and specific controllable directions (represented by its eigenvectors). As a proof of concept, we applied our method to transcranial magnetic stimulationinduced electroencephalographic responses across four motor-related states and two resting states. We found that states such as open-eye rest, closed-eye rest, and motor-related states were more effectively differentiated using controllable directions than overall controllability. However, certain states, like motor execution and motor imagery, remained indistinguishable using these measures. These findings indicate that some brain states differ in their intrinsic control properties as dynamical systems, while others share similarities that make them less distinguishable. This study underscores the value of control theory-based analyses in quantitatively how intrinsic brain states shape the brains responses to stimulation, providing deeper insights into the dynamic properties of these states. This methodology holds promise for diverse applications, including characterizing individual response variability and identifying conditions for optimal stimulation efficacy. Significant statementThe brain can be viewed as a control system transitioning between states, such as from rest to motor activity. Previous studies using network control theory mostly evaluated brain dynamics without external perturbations, neglecting their role in accurate system identification. This study integrates perturbation inputs with network control theory to propose a method for estimating the controllability Gramian, thereby providing insights into brain dynamics. We applied this approach to TMS-induced EEG responses in motor-related and resting states. Our findings show that controllable directions (eigenvectors) allow better discrimination between states than overall controllability. Our method can quantitatively assess brain state differences, and has potential applications in characterizing individual response variability and optimizing stimulation efficacy.

neuroscience↗

bioRxiv2025

Electroencephalographic responses before, during, and after upper limb paired associative stimulation

Paired associative stimulation (PAS) is a non-invasive protocol involving repeated stimulus pairs to activate two cortical areas alternately, inducing Hebbian-like plasticity. However, its neurophysiological impacts remain unclear. To determine the changes that occur in the brain during PAS, brain activity during PAS must be measured and distinguished from the electromagnetic artifacts produced by the stimulation. Here, we present a novel dataset of electroencephalography (EEG) measurements during PAS with an inter-stimulus-interval of 25 ms (PAS25, expected to induce long-term potentiation-like changes) or 35 ms (PAS35, no expected change). This dataset includes raw data and pre-processed data with electromagnetic artefacts removed. he right ulnar nerves electrical stimulation preceded transcranial magnetic stimulation to the left primary motor cortex in both cases. EEG was measured before and after the PAS sessions, with only electrical or magnetic stimulation. To demonstrate the quality of the data, we summarize the stability of the stimulation site and the event-related potentials before, during, and after PAS. This dataset will enable observing brain dynamics due to the accumulation of stimulations during PAS and differences in responsiveness to stimulations before and after PAS.

neuroscience↗