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There is a growing research interest to extract the temporal dependency between brain networks. Among several existing methods, functional network connectivity (FNC) is one of the widely used approaches to capture the intrinsic functional relationships among brain networks. In this study, we introduced a novel approach that uses FNC matrices of Adolescent Brain and Cognitive Development (ABCD) data to evaluate multiple overlapping brain functional change patterns (FCPs). Results show several highly structured FCPs that have a significant change over a two-year period and become stronger with age including brain functional connectivity between visual (VS) and sensorimotor (SM) domains. Our approach is a powerful tool to visualize and evaluate patterns of whole brain functional changes in longitudinal data.

We present the results of a search for core-collapse supernova neutrinos, using long-term KamLAND data from 2002 March 9 to 2020 April 25. We focus on the electron antineutrinos emitted from supernovae in the energy range of 1.8–111 MeV. Supernovae will make a neutrino event cluster with the duration of ∼10 s in the KamLAND data. We find no neutrino clusters and give the upper limit on the supernova rate to be 0.15 yr⁻¹with a 90% confidence level. The detectable range, which corresponds to a >95% detection probability, is 40–59 kpc and 65–81 kpc for core-collapse supernovae and failed core-collapse supernovae, respectively. This paper proposes to convert the supernova rate obtained by the neutrino observation to the Galactic star formation rate. Assuming a modified Salpeter-type initial mass function, the upper limit on the Galactic star formation rate is <(17.5–22.7)M_⊙yr⁻¹with a 90% confidence level.

Abstract We present the results of a time-coincident event search for low-energy electron antineutrinos in the KamLAND detector with gamma-ray bursts (GRBs) from the Gamma-ray Coordinates Network and Fermi Gamma-ray Burst Monitor. Using a variable coincidence time window of ±500 s plus the duration of each GRB, no statistically significant excess above the background is observed. We place the world’s most stringent 90% confidence level upper limit on the electron antineutrino fluence below 17.5 MeV. Assuming a Fermi–Dirac neutrino energy spectrum from the GRB source, we use the available redshift data to constrain the electron antineutrino luminosity and effective temperature.

We report on a search for electron antineutrinos (${\overline{\nu }}_e$) from astrophysical sources in the neutrino energy range 8.3–30.8 MeV with the KamLAND detector. In an exposure of 6.72 kton-year of the liquid scintillator, we observe 18 candidate events via the inverse beta decay reaction. Although there is a large background uncertainty from neutral current atmospheric neutrino interactions, we find no significant excess over background model predictions. Assuming several supernova relic neutrino spectra, we give upper flux limits of 60–110 cm⁻²s⁻¹(90% confidence level, CL) in the analysis range and present a model-independent flux. We also set limits on the annihilation rates for light dark matter pairs to neutrino pairs. These data improve on the upper probability limit of⁸B solar neutrinos converting into${\overline{\nu }}_e$,$P_{{\nu }_e\to {\overline{\nu }}_e}<3.5\times {10}^{-5}$(90% CL) assuming an undistorted${\overline{\nu }}_e$shape. This corresponds to a solar${\overline{\nu }}_e$flux of 60 cm⁻²s⁻¹(90% CL) in the analysis energy range.

We report the result of a search for neutrinos in coincidence with solar flares from the GOES flare database. The search was performed on a 10.8 kton-year exposure of KamLAND collected from 2002 to 2019. This large exposure allows us to explore previously unconstrained parameter space for solar flare neutrinos. We found no statistical excess of neutrinos and established 90% confidence level upper limits of 8.4 × 10⁷cm⁻²(3.0 × 10⁹cm⁻²) on the electron antineutrino (electron neutrino) fluence at 20 MeV normalized to the X12 flare, assuming that the neutrino fluence is proportional to the X-ray intensity.