The　dynamic　description　of　neural　networks　has　attracted　the　attention　of　researchers　for　dynamic　networks　may　carry　more　information　compared　with　resting-state　networks.　As　a　non-invasive　electrophysiological　data　with　high　temporal　and　spatial　resolution,　magnetoencephalogram　(MEG)　can　provide　rich　information　for　the　analysis　of　dynamic　functional　brain　networks.　In　this　review,　the　development　of　MEG　brain　network　was　summarized.　Several　analysis　methods　such　as　sliding　window,　Hidden　Markov　model,　and　time-frequency　based　methods　used　in　MEG　dynamic　brain　network　studies　were　discussed.　Finally,　the　current　research　about　multi-modal　brain　network　analysis　and　their　applications　with　MEG　neurophysiology,　which　are　prospected　to　be　one　of　the　research　directions　in　the　future,　were　concluded.