Anterior Cingulate Cortex Signals the Need to Control Intrusive Thoughts during Motivated Forgetting.

How do people limit awareness of unwanted memories? When such memories intrude, a control process engages the right DLPFC (rDLPFC) to inhibit hippocampal activity and stop retrieval. It remains unknown how the need for control is detected, and whether control operates proactively to prevent unwelcome memories from being retrieved, or responds reactively, to counteract intrusions. We hypothesized that dorsal ACC (dACC) detects the emergence of an unwanted trace in awareness and transmits the need for inhibitory control to rDLPFC. During a memory suppression task, we measured in humans (both sexes) trial-by-trial variations in the theta power and N2 amplitude of dACC, two EEG markers that are thought to reflect the need for control. With simultaneous EEG-fMRI recordings, we tracked interactions among dACC, rDLPFC, and hippocampus during suppression. We found a clear role of dACC in detecting the need for memory control and upregulating prefrontal inhibition. Importantly, we identified distinct early (300-450 ms) and late (500-700 ms) dACC contributions, suggesting both proactive control before recollection and reactive control in response to intrusions. Stronger early activity was associated with reduced hippocampal activity and diminished BOLD signal in dACC and rDLPFC, suggesting that preempting retrieval reduced overall control demands. In the later window, dACC activity was larger, and effective connectivity analyses revealed robust communication from dACC to rDLPFC and from rDLPFC to hippocampus, which are tied to successful forgetting. Together, our findings support a model in which dACC detects the emergence of unwanted content, triggering top-down inhibitory control, and in which rDLPFC countermands intruding thoughts that penetrate awareness.SIGNIFICANCE STATEMENT Preventing unwanted memories from coming to mind is an adaptive ability of humans. This ability relies on inhibitory control processes in the prefrontal cortex to modulate hippocampal retrieval processes. How and when reminders to unwelcome memories come to trigger prefrontal control mechanisms remains unknown. Here we acquired neuroimaging data with both high spatial and temporal resolution as participants suppressed specific memories. We found that the anterior cingulate cortex detects the need for memory control, responding both proactively to early warning signals about unwelcome content and reactively to intrusive thoughts themselves. When unwanted traces emerge in awareness, anterior cingulate communicates with prefrontal cortex and triggers top-down inhibitory control over the hippocampus through specific neural oscillatory networks.

Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines.

The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera.

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall.

Human hippocampal theta oscillations play a key role in accurate spatial coding. Associative encoding involves similar hippocampal networks but, paradoxically, is also characterized by theta power decreases. Here, we investigated how theta activity relates to associative encoding of place contexts resulting in accurate navigation. Using MEG, we found that slow-theta (2-5Hz) power negatively correlated with subsequent spatial accuracy for virtual contextual locations in posterior hippocampus and other cortical structures involved in spatial cognition. A rare opportunity to simultaneously record MEG and intracranial EEG in an epilepsy patient provided crucial insights: during power decreases, slow-theta in right anterior hippocampus and left inferior frontal gyrus phase-led the left temporal cortex and predicted spatial accuracy. Our findings indicate that decreased slow-theta activity reflects local and long-range neural mechanisms that encode accurate spatial contexts, and strengthens the view that local suppression of low-frequency activity is essential for more efficient processing of detailed information.

Working memory processes are mediated by local and long-range synchronization of alpha oscillations.

Different cortical dynamics of alpha oscillations (8-13 Hz) have been associated with increased working memory load, which have been mostly interpreted as a neural correlate of functional inhibition. This study aims at determining whether different manifestations of load-dependent amplitude and phase dynamics in the alpha band can coexist over different cortical regions. To address this question, we increased information load by manipulating the number and spatial configuration of domino spots. Time-frequency analysis of EEG source activity revealed (i) load-independent increases of both alpha power and interregional alpha-phase synchrony within task-irrelevant, posterior cortical regions and (ii) load-dependent decreases of alpha power over areas of the left pFC and bilateral posterior parietal cortex (PPC) preceded in time by load-dependent decreases of alpha-phase synchrony between the left pFC and the left PPC. The former results support the role of alpha oscillations in inhibiting irrelevant sensorimotor processing, whereas the latter likely reflect release of parietal task-relevant areas from top-down inhibition with load increase. This interpretation found further support in a significant latency shift of 15 msec from pFC to the PPC. Together, these results suggest that amplitude and phase alpha dynamics in both local and long-range cortical networks reflect different neural mechanisms of top-down control that might be crucial in mediating the different working memory processes.

Effects of semantic relatedness on age-related associative memory deficits: the role of theta oscillations.

Growing evidence suggests that age-related deficits in associative memory are alleviated when the to-be-associated items are semantically related. Here we investigate whether this beneficial effect of semantic relatedness is paralleled by spatio-temporal changes in cortical EEG dynamics during incidental encoding. Young and older adults were presented with faces at a particular spatial location preceded by a biographical cue that was either semantically related or unrelated. As expected, automatic encoding of face-location associations benefited from semantic relatedness in the two groups of age. This effect correlated with increased power of theta oscillations over medial and anterior lateral regions of the prefrontal cortex (PFC) and lateral regions of the posterior parietal cortex (PPC) in both groups. But better-performing elders also showed increased brain-behavior correlation in the theta band over the right inferior frontal gyrus (IFG) as compared to young adults. Semantic relatedness was, however, insufficient to fully eliminate age-related differences in associative memory. In line with this finding, poorer-performing elders relative to young adults showed significant reductions of theta power in the left IFG that were further predictive of behavioral impairment in the recognition task. All together, these results suggest that older adults benefit less than young adults from executive processes during encoding mainly due to neural inefficiency over regions of the left ventrolateral prefrontal cortex (VLPFC). But this associative deficit may be partially compensated for by engaging preexistent semantic knowledge, which likely leads to an efficient recruitment of attentional and integration processes supported by the left PPC and left anterior PFC respectively, together with neural compensatory mechanisms governed by the right VLPFC.

Theta band zero-lag long-range cortical synchronization via hippocampal dynamical relaying.

Growing evidence suggests that synchronization among distributed neuronal networks underlie functional integration in the brain. Neural synchronization is typically revealed by a consistent phase delay between neural responses generated in two separated sources. But the influence of a third neuronal assembly in that synchrony pattern remains largely unexplored. We investigate here the potential role of the hippocampus in determining cortico-cortical theta synchronization in different behavioral states during motor quiescent and while animals actively explore the environment. To achieve this goal, the two states were modeled with a recurrent network involving the hippocampus, as a relay element, and two distant neocortical sites. We found that cortico-cortical neural coupling accompanied higher hippocampal theta oscillations in both behavioral states, although the highest level of synchronization between cortical regions emerged during motor exploration. Local field potentials recorded from the same brain regions qualitatively confirm these findings in the two behavioral states. These results suggest that zero-lag long-range cortico-cortical synchronization is likely mediated by hippocampal theta oscillations in lower mammals as a function of cognitive demands and motor acts.

Semantic congruence enhances memory of episodic associations: role of theta oscillations.

Growing evidence suggests that theta oscillations play a crucial role in episodic encoding. The present study evaluates whether changes in electroencephalographic theta source dynamics mediate the positive influence of semantic congruence on incidental associative learning. Here we show that memory for episodic associations (face-location) is more accurate when studied under semantically congruent contexts. However, only participants showing RT priming effect in a conceptual priming test (priming group) also gave faster responses when recollecting source information of semantically congruent faces as compared with semantically incongruent faces. This improved episodic retrieval was positively correlated with increases in theta power during the study phase mainly in the bilateral parahippocampal gyrus, left superior temporal gyrus, and left lateral posterior parietal lobe. Reconstructed signals from the estimated sources showed higher theta power for congruent than incongruent faces and also for the priming than the nonpriming group. These results are in agreement with the attention to memory model. Besides directing top-down attention to goal-relevant semantic information during encoding, the dorsal parietal lobe may also be involved in redirecting attention to bottom-up-driven memories thanks to connections between the medial-temporal and the left ventral parietal lobe. The latter function can either facilitate or interfere with encoding of face-location associations depending on whether they are preceded by semantically congruent or incongruent contexts, respectively, because only in the former condition retrieved representations related to the cue and the face are both coherent with the person identity and are both associated with the same location.

Muscle artifact removal from human sleep EEG by using independent component analysis.

Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.