Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range

Hata, J.; Nakae, K.; Tsukada, H.; Woodward, A.; Haga, Y.; Iida, M.; Uematsu, A.; Seki, F.; Ichinohe, N.; Gong, R.; Kaneko, T.; Yoshimaru, D.; Watakabe, A.; Abe, H.; Tani, T.; skibbe, h.; Maeda, M.; Papazian, F.; Hagiya, K.; Kishi, N.; Shimogori, T.; Yamamori, T.; Okano, H. j.; Okano, H.

doi:10.1101/2022.09.21.508952

Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.09.21.508952

Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range

View original source ↗Export metadata ↓

Abstract

Magnetic resonance imaging (MRI) is a noninvasive neuroimaging method beneficial for the identification of normal developmental and aging processes and data sharing. Marmosets have a relatively shorter life expectancy (approximately 10 years) than other primates, including, humans because they grow and age faster. Hence, the common marmoset model is effective in aging research. The current study investigated the aging process of the marmoset brain and provided an open MRI database on marmosets with a wide age range. The Brain/MINDS Marmoset Brain MRI Dataset contains brain MRI information on 216 marmosets aged between 1 and 10 years. During its release date, it is the largest public dataset worldwide. Further, it comprises multi contrast MRI images. In addition, 91 of 216 animals have corresponding ex vivo high-resolution MRI datasets. Our MRI database, which is available at the Brain/MINDS Data portal might help understand the effects of different factors, such as age, sex, body size, and fixation, on the brain. Moreover, it can contribute to and accelerate brain science studies worldwide.

Source connections

Published article ↗

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Hata, J., Nakae, K., Tsukada, H., Woodward, A., Haga, Y., Iida, M., Uematsu, A., Seki, F., Ichinohe, N., Gong, R., Kaneko, T., Yoshimaru, D., Watakabe, A., Abe, H., Tani, T., skibbe, h., Maeda, M., Papazian, F., Hagiya, K., Kishi, N., Shimogori, T., Yamamori, T., Okano, H. j., Okano, H.. 2022-09-22. Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range. https://doi.org/10.1101/2022.09.21.508952

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Novel Tau Interaction Partners as Potential Biomarkers and Therapeutic Targets in Neurodegenerative Diseases

Alzheimers disease is a progressive neurodegenerative disorder leading to cognitive decline and ultimately death. One of the main hallmarks of this disease is the accumulation of pathological tau protein into neurofibrillary tangles. To better understand these pathological mechanisms, it is essential to characterize the tau protein interactome in health and disease. In this study, we performed a yeast two-hybrid screening using a human brain cDNA library, from which we identified 15 novel tau-interacting proteins that bind to full-length human tau, or its fragments: the N-terminus, microtubule-binding domain, or C-terminus. We subsequently validated these novel interactions using in vitro co-immunoprecipitation and colocalization assays with both physiological and pathological tau isoforms. Furthermore, their interactions with the human tau protein were analyzed using surface plasmon resonance. Finally, an in silico interactome analysis of these newly discovered tau-binding partners revealed several additional interactors that can link them to downstream processes critical to neurodegenerative pathways Our findings offer fresh insights into tau biology and highlight novel targets for therapeutic intervention and biomarker development.

neuroscience↗

bioRxiv2026

Early visual cortex is not functionally necessary for temporal attention: A TMS study

Temporal attention enhances the selective processing of information at a specific point in time and improves perception. Previous neuroimaging studies investigating its neural mechanisms have either conflated temporal attention with temporal expectation or provided correlational evidence. Thus, whether early visual cortex is functionally necessary for temporal attention remains unknown. Here, we combined psychophysics with transcranial magnetic stimulation (TMS) to examine whether V1/V2 are functionally necessary and play a causal role in temporal attention. Observers performed an orientation-discrimination task in which two consecutive stimuli, T1 and T2, were presented in the periphery, preceded by a precue providing either valid or neutral temporal information. Double-pulse TMS was delivered under four conditions: at T1 (T1-TMS), at T2 (T2-TMS), at both T1 and T2 (both-TMS), or not at all (none-TMS), to assess its effects on temporal attention and the efficacy of the stimulation was verified by pupil size analysis. Behavioral results revealed robust temporal attention benefits on d', reaction time, and the Balanced Integration Score (BIS) across all four TMS conditions, indicating that the benefits of temporal attention were unaffected by occipital TMS. Furthermore, pupil dilation was largest in the both-TMS condition, followed by the T1-TMS and T2-TMS conditions, and smallest in the none-TMS condition, providing physiological verification of the stimulation protocol. Together, these findings reveal the first causal evidence that the early visual cortex is not functionally necessary for endogenous temporal attention.

neuroscience↗

bioRxiv2026

Disentanglement of persistent and dynamic neural substrates of working memory

Competing theories of working memory have alternatively emphasized the importance of persistent and dynamic substrates of this cognitive function. Here, we directly tested these theories by computationally isolating the contributions of persistent and dynamic activity to working memory. We analyzed neuronal population activity recordings in macaques doing match/non-match working memory tasks. We first extracted signatures of persistent and dynamic activity through cue-specific temporal means, temporal principal components, and correlations of activity patterns across task epochs. We then generated synthetic data constrained by these signatures, and decoded cue stimuli from these data, separately from persistent and non-persistent neurons. We found that high decoding performance arose from a nuanced interplay of persistent and dynamic activity. Specifically, we could decode stimuli at empirical or above-chance levels across all tasks only from persistent neurons, but only after constraining correlations of activity patterns across epochs. In this way, we showed distinct roles for persistent and dynamic neuronal activity that help to unify two competing theories of working memory. More broadly, we also advanced computational controlled experiments as a principled framework for adjudicating between competing models of neuronal population activity.

neuroscience↗