Multi-band EPI, in the blipped-CAIPI scheme, addresses critical limitations to fMRI resolution and fidelity from conventional EPI [1] by increasing imaging speed. In this educational talk we will review methods, benefits, costs, and variations of the multi-band approach. Multi-band EPI images multiple slices simultaneously by multiplexing excitations across interspaced frequency bands corresponding to target slices. The blipped approach adds alternating phase offsets to these slices from center in either direction by some fraction of field of view (FOV). Leveraging coil sensitivity maps, a multi-channel reconstruction with a GRAPPA or SENSE kernel then yields well-separated slice images. This strategy leads to a factor-N speed-up of volume acquisition [2]. Critically, partially overlapping slices are quite unique from each other, k-space lines are not skipped, and thus coils richly sample separable signals. Resultantly, reconstructed images are associated with low noise penalty (G-factor much lower than factor-N), enabling high factor-N accelerations [3]. In contrast, conventional fMRI in-plane acceleration of single-shot/slice/echo EPI, while also using SENSE/GRAPPA reconstruction, accelerates by skipping alternate k-space lines, and high-density receiver arrays sample signals of only one slice. This in-plane acceleration leads to approximately $$$\sqrt{N}$$$ noise penalty for less than factor-N speed-up, with N>2 leading to substantial losses. Thus, on its own, multi-band represents a critical innovation for accelerating fMRI and is a major advance over in-plane acceleration.We will review how increased temporal resolution is a key benefit of multi-band EPI. Conventionally BOLD signal is considered low temporal frequency, but greater temporal resolution is argued to better dealias/disentangle overlapping signals from cardiac and respiratory sources that are of higher frequency (than conventional EPI acquisition Nyquist) [4]. In the context of task fMRI however, the specific shape of hemodynamic response is known to be informative of pathological or pharmacological states, which is explained in higher frequency temporal components, and so faster sampling can be directly beneficial for characterizing BOLD [5]. In the context of resting state, with the increasing focus on dynamics [6], it is also important to realize that the capture of non-stationary transitions (spikes, sharp waves, etc.) also require high temporal frequencies for characterization, highlighting another benefit for temporally accelerated BOLD imaging.We will review spatial resolution considerations of multi-band EPI. For a fixed experiment duration, there are fewer total readouts, and more time can be spent per readout (i.e. slice-set), enabling higher spatial resolution. In this way multi-band EPI nominally enables high spatial resolution for fMRI, but important considerations are required on experiment design and analysis towards determining the optimal increase in spatial resolution. This is because fMRI sensitivity, calculated as temporal signal-to-noise ratio (tSNR), scales linearly with voxel size. As especially relevant to SPM-style univariate analyses, multi-band EPI does not mitigate the increase in thermal noise associated with higher resolution (by sampling signal of a given voxel location less densely), and image smoothing to attenuate thermal noise obviates high-resolution acquisition and penalizes false discovery rate estimation. However, in multivariate decomposition, typified by independent component analysis (ICA), tSNR's fixed-point sensitivity measure is considered less relevant, and aggregate power tSNRx$$$\sqrt{N}$$$ for resolving signal components by (sparsity-weighted) statistical contrast is increasingly considered more relevant [7]. We will also consider how in the context of population studies, anatomical variation of the neural correlates of function sets an upper bound to the benefits of spatial resolution increases.We will then review the more recent development of multi-echo multi-band (MEMB) fMRI, and analysis with multi-echo independent components analysis, in its own right and in relation to single-echo multi-band EPI. Multi-echo fMRI enables relaxometric assessments of BOLD signals, with benefits ranging from susceptibility artifact compensation, to thermal noise reduction without smoothing, to non-BOLD component removal, and time series output in dT2* units [8]. We will discuss how MEMB combines these benefits with additional ones: specifically determining the statistical dimensionality of total and BOLD spaces, isolation of thermal noise, and signal denoising (including slice leakage, cardiac/respiratory, GRAPPA artifacts) without template-matching algorithms such as FSL FIX, at 3T and 7T [9-11].Finally, we will review the multi-band acquisition strategy of the Human Connectome Project (HCP). After summarizing acquisition parameters and the current workflow, we will consider implications of the wider NIH objective of harmonization of neuroimaging with HCP-like protocols. In particular, it will be emphasized that HCP multi-band EPI parameters are designed with a noise tolerance based on 1-hour resting state acquisition [12]. In clinical or neurodevelopmental contexts, however, this strategy may be disagreeable, so alternative multi-band configurations will be considered.