Biology subjects

Hirayama, J.-i.

Publications and source records attributed to Hirayama, J.-i..

4 recordsLinked to original sources

bioRxiv2023

Cognitive Dynamics Estimation: A whole-brain spatial regression paradigm for extracting the temporal dynamics of cognitive processes

Functional MRI (fMRI) has been instrumental in understanding how cognitive processes are spatially mapped in the brain, yielding insights about brain regions and functions. However, in case the orthogonality of behavioral or stimulus timing is not guaranteed, the estimated brain maps fail to dissociate each cognitive process, and the resultant maps become unstable. Also, the brain mapping exercise can not provide temporal information on the cognitive process. Here we propose a qualitatively different approach to fMRI analysis, named Cognitive Dynamics Estimation (CDE), that estimates how multiple cognitive processes change over time even when behavior or stimulus logs are unavailable. This method transposes the conventional brain mapping; the brain activity pattern at each time point is subject to regression analysis with data-driven maps of cognitive processes as regressors, resulting in the time series of cognitive processes. The estimated time series captured the fluctuation of intensity and timing of cognitive processes on a trial-by-trial basis, which conventional analysis could not capture. Notably, the estimated time series predicted participants cognitive ability to perform each psychological task. As an addition to our fMRI analytic toolkit, these results suggest the potential for CDE to elucidate underexplored cognitive phenomena, especially in the temporal domain. HighlightsWe propose a novel fMRI analysis that are equivalently effective to the brain mapping approach The temporal dynamics of multiple cognitive processes were captured on a trial-by-trial basis. This analysis can be applied data-driven even when the researchers do not have any hypothesis about what kind of cognitive processes are recruited in the task. This analysis can be applied even when the cognitive processes are strongly correlated in the temporal domain. The estimated temporal dynamics well predicted individuals cognitive ability required for each task.

neuroscience↗

bioRxiv2021

EEG-based neurofeedback with network components extraction: data-driven approach by multilayer ICA extension and simultaneous EEG-fMRI measurements

Several studies have reported advanced treatments for depressive symptoms, such as real-time neurofeedback (NF) with functional MRI (fMRI) and/or electroencephalogram (EEG). NF focusing on a regularization of brain activity associated with the amygdala or functional connectivity (FC) between the executive control network (ECN) and default mode network (DMN) has been applied to reduce depressive symptoms. However, it is practically difficult to install the fMRI-NF system and to consistently provide treatment, because of high cost. Additionally, no practical signal processing techniques have been developed extracting FC-related features from EEG signals, particularly when no physical forward models are available. In this regard, stacked pooling and linear components estimation (SPLICE), recently proposed as a multilayer extension of independent component analysis (ICA) and related independent subspace analysis (ISA), can be a promising alternative. The resting-state EEG network features can be correlated with fMRI network activity corresponding to the DMN or ECN. This may enable the modulation of the target FC-related features in EEG-based NF. In this study, we developed a real-time EEG NF system for improving depressive symptoms by using the SPLICE. Utilizing information from the fMRI biomarkers, we evaluated our paradigm for effectiveness with regard to upregulation of the dorsolateral prefrontal cortex /middle frontal gyrus or downregulation of the precuneus/posterior cingulate cortex. We conducted an NF experiment in participants with subclinical depression; the participants were divided into the NF group (n=8) and the sham group (n=9). We found a significant reduction and a large effect size in the rumination response scale (RRS) score (reflection) in the NF group, compared to the sham group. However, we did not find a significant relationship between the training score and difference in symptoms. This suggests that increased controllability of the EEG signals did not directly reduce the RRS reflection score. This could be due to various reasons such as improper feature extraction, individual differences, and the targeted brain regions. In this paper, we also discuss the possible ways to modify our NF protocol including the design of the experiment, sample size, and online processing. We then discuss way to improve the NF training, based on our results.

neuroscience↗

bioRxiv2020

Asymmetric effective connectivity within frontoparietal motor network underlying motor imagery and motor execution

Both imagery and execution of motor controls consist of interactions within a neuronal network, including frontal motor-related regions and posterior parietal regions. To reveal neural representation in the frontoparietal motor network, two approaches have been proposed thus far: one is decoding of actions/modes related to motor control from the spatial pattern of brain activity; another is to estimate directed functional connectivity, which means a directed association between two brain regions within motor regions. However, directed connectivity among multiple regions of the motor network during motor imagery (MI) or motor execution (ME) has not been investigated. Here, we attempted to characterize the directed functional connectivity within the frontoparietal motor-related networks between the MI and ME conditions. We developed a delayed sequential movement and imagery task to evoke brain activity associated with data under ME and MI via functional magnetic resonance imaging scanning. We applied a causal discovery approach, linear non-Gaussian acyclic causal model, to identify directed functional connectivity among the frontoparietal motor-related brain regions for each condition. We demonstrated higher directed functional connectivity from the contralateral dorsal premotor cortex (dPMC) to the primary motor cortex (M1) in ME than in MI. We mainly identified significant direct effects of the dPMC and ventral premotor cortex (vPMC) to the parietal regions. In particular, connectivity from the dPMC to the superior parietal lobule in the same hemisphere showed significant positive effects across all conditions. Contrastingly, interlateral connectivities from the vPMC to the superior parietal lobule showed significantly negative effects across all conditions. Finally, we found positive effects from A1 to M1 in the same hemisphere, such as the audio-motor pathway. These results indicated that the sources of motor command originated in d/vPMC influenced M1 and parietal regions as achieving ME and MI. Additionally, sequential sounds may functionally facilitate temporal motor processes.

neuroscience↗

bioRxiv2018

State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes

Recent functional magnetic resonance imaging (fMRI) studies have increasingly revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have been inherently limited to state-specific characterizations of related brain networks and their functions, several recent studies have examined the potential state-unspecific nature of functional brain networks, such as their global similarities across different experimental conditions (i.e., states) including both task and rest. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variation in whole-brain functional connectivity patterns, called \"Common Neural Modes (CNMs)\", from a large fMRI dataset including eight task/rest states, obtained from the Human Connectome Project. Furthermore, we tested how CNMs account for variability in individual behavioral measures. The results revealed that three CNMs were robustly extracted under various different preprocessing conditions. Each of these CNMs was significantly correlated with different aspects of behavioral measures of both fluid and crystalized intelligence. The three CNMs were also able to predict several life outcomes, such as income and life satisfaction, achieving the highest performance when combined with behavioral intelligence measures as inputs. Our findings highlight the importance of state-unspecific brain networks to characterize fundamental individual variation.

neuroscience↗