The limits of human visual short-term memory (VSTM) have been well documented, and recent neuroscientific studies suggest that VSTM performance is associated with activity in the posterior parietal cortex. Here we show that artificially elevating parietal activity via positively charged electric current through the skull can rapidly and effortlessly improve people's VSTM performance. This artificial improvement, however, comes with an interesting twist: it interacts with people's natural VSTM capability such that low performers who tend to remember less information benefitted from the stimulation, whereas high performers did not. This behavioral dichotomy is explained by event-related potentials around the parietal regions: low performers showed increased waveforms in N2pc and contralateral delay activity (CDA), which implies improvement in attention deployment and memory access in the current paradigm, respectively. Interestingly, these components are found during the presentation of the test array instead of the retention interval, from the parietal sites ipsilateral to the target location, thus suggesting that transcranial direct current stimulation (tDCS) was mainly improving one's ability to suppress no-change distractors located on the irrelevant side of the display during the comparison stage. The high performers, however, did not benefit from tDCS as they showed equally large waveforms in N2pc and CDA, or SPCN (sustained parietal contralateral negativity), before and after the stimulation such that electrical stimulation could not help any further, which also accurately accounts for our behavioral observations. Together, these results suggest that there is indeed a fixed upper limit in VSTM, but the low performers can benefit from neurostimulation to reach that maximum via enhanced comparison processes, and such behavioral improvement can be directly quantified and visualized by the magnitude of its associated electrophysiological waveforms.

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.

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The interaction between goal-directed and stimulus-driven attentional control allows humans to rapidly reorient to relevant objects outside the focus of attention--a phenomenon termed contingent reorienting. Neuroimaging studies have observed activation of the ventral and dorsal attentional networks, but specific involvement of each network remains unclear. The present study aimed to determine whether both networks are critical to the processes of top-down contingent reorienting. To this end, we combined the contingent attentional capture paradigm with the use of transcranial magnetic stimulation (TMS) to interfere with temporoparietal junction (TPJ; ventral network) and frontal eye field (dorsal network) activity. The results showed that only right TPJ (rTPJ) TMS modulated contingent orienting. Furthermore, this modulation was highly dependent on visual fields: rTPJ TMS increased contingent capture in the left visual field and decreased the effect in the right visual field. These results demonstrate a critical involvement of the ventral network in attentional reorienting and reveal the spatial selectivity within such network.

How does the brain enable us to remember two or more object representations in visual working memory (VWM) without confusing them? This “gluing” process, or feature binding, refers to the ability to join certain features together while keeping them segregated from others. Recent neuroimaging research has reported higher BOLD response in the left temporal and parietal cortex during a binding-VWM task. However, less is known about how the two regions work in synchrony to support such process. In this study, we applied transcranial alternating current stimulation (tACS) over the left temporal and parietal cortex in gamma and theta frequency, with a phase difference of either 0° (in-phase) or 180° (anti-phase) to account for the different ways through which neural synchronization may occur. We found no facilitatory or inhibitory effect from sham, theta, and in-phase gamma stimulation. Importantly, there was an enhancement effect from anti-phase gamma tACS that was binding-specific, and such effect was only apparent in low-performing individuals who had room for improvement. Together, these results demonstrate that binding-VWM is supported by a temporally-precise oscillatory mechanism within the gamma frequency range, and that the advantageous 180°-apart phase relationship also implies a possible temporal driver-to-receiver time-lag between the temporal and parietal cortex.

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. 'Normalised shapes' can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

Natural sensory signals have nonlinear structures dynamically composed of the carrier frequencies and the variation of the amplitude (i.e., envelope). How the human brain processes the envelope information is still poorly understood, largely due to the conventional analysis failing to quantify it directly. Here, we used a recently developed method, Holo-Hilbert spectral analysis, and steady-state visually evoked potential collected using electroencephalography (EEG) recordings to investigate how the human visual system processes the envelope of amplitude-modulated signals, in this case with a 14 Hz carrier and a 2 Hz envelope. The EEG results demonstrated that in addition to the fundamental stimulus frequencies, 4 Hz amplitude modulation residing in 14 Hz carrier and a broad range of carrier frequencies covering from 8 to 32 Hz modulated by 2 Hz amplitude modulation are also found in the two-dimensional frequency spectrum, which have not yet been recognized before. The envelope of the stimulus is also found to dominantly modulate the response to the incoming signal. The findings thus reveal that the electrophysiological response to amplitude-modulated stimuli is more complex than could be revealed by, for example, Fourier analysis. This highlights the dynamics of neural processes in the visual system.