Publications

Ageing produces changes in brain function. To map this process, researchers increasingly utilise Magnetoencephalography (MEG) to identify spectral shifts across large, age-diverse cohorts. However most such research has used cross-sectional designs, which may be confounded by generational differences and high inter-individual variability. To address these limitations, we analysed longitudinal resting-state MEG data from the Cam-CAN cohort, an adult lifespan cohort with a repeat MEG recording after approximately a decade. This design allowed us to contrast cross-sectional age differences between people (at baseline) with longitudinal age changes within people. Our analyses revealed four key findings. First, we observed consistency between baseline and longitudinal effects of increasing age on the slowing of the Alpha peak frequency. Second, we identified non-linear changes in Beta power, whereby power increased from youth to mid-life, but decreased from mid-life to late-life. These findings validate the use of normative charts for mapping these spectral components. Third, we detected some longitudinal effects, such as power reductions in Alpha and slowing of Beta peak frequency, that were not apparent cross-sectionally, underscoring the importance of intra-individual designs. Fourth, we did not find the commonly reported age-related flattening of the aperiodic exponent of the power spectrum, except in direct measures of cardiac activity. Together, these results demonstrate that while cross-sectional data capture major trends, longitudinal data are essential for isolating the true neurophysiological signatures of the ageing process.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) started in 2010 to study the effect of healthy adult ageing on cognition and the brain in a population-derived sample. The study design and protocol for Phases 1–3 of Cam-CAN were detailed in 10.1186/s12883-014-0204-1; this paper outlines the design and protocol of Phases 4–5, which enable longitudinal investigation of cognitive and brain ageing over approximately 12 years. More details about the Cam-CAN project can be found here: www.cam-can.org. Phase 4 was an at-home assessment of cognition, demographics and lifestyle, performed approximately 6 years after Phase 1 (baseline assessment), for which all people from Phase 1 were invited. Phase 5 combined repeated online cognitive, demographics and lifestyle assessment, followed by in-lab attendance for MRI and MEG brain scanning, approximately 12 years after Phase 1, for which all people from Phase 2 (baseline brain assessment) were invited. Demographics, lifestyle and cognitive data are therefore now available for three timepoints, and MRI and MEG brain data for two timepoints. The Cam-CAN study offers deep and wide phenotyping of neurocognitive health across the adult lifespan (18–96). These rich data will allow researchers to address questions like: why do some people maintain their cognitive abilities better than others, in terms of their brain structure or function, their lifestyle and/or their genetics? Given the shifting demographics towards old age in most countries, this knowledge will be important to help people function independently for longer, reducing both individual and societal burden.

Understanding the organizing principles of brain activity can advance neurotechnology and medical diagnosis. Traditionally, neural activity is viewed as consisting oscillations in distinct frequency bands. However, emerging evidence suggests these oscillations often manifest as transient bursts rather than sustained rhythms. We examine the hypothesis that rhythmicity (sustained vs bursty) adds a further dimension to brain organization. Using a rhythmicity measure, we segment neurophysiological spectra from 859 participants across datasets, species, recording techniques, ages 18-88, sexes, brain regions, and cognitive states in health and disease. Our results reveal a universal rhythmicity-resolved spectral architecture with two categories: high-rhythmicity bands exhibiting sustained oscillations and new low-rhythmicity bands dominated by brief bursts. This architecture reflects two modes of operation: sustained bands suitable for maintaining ongoing activity, and transient bands which can signal responses to change. The rhythmicity-resolved architecture provides a unifying framework that bridges human and non-human findings, enables individualized spectral definitions, and offers a principled basis for understanding brain activity.

Controlling action and thought requires the capacity to stop mental processes. Over the past two decades, evidence has grown that a domain-general inhibitory control mechanism supported by the right lateral prefrontal cortex achieves these functions. However, current views of the neural mechanisms of inhibitory control derive largely from research into the stopping of action. Whereas action stopping is a convenient empirical model, it does not invoke thought inhibition and cannot be used to identify the unique features~of this process. Here, we review research that addresses how organisms stop a key process that drives thoughts: memory retrieval. This work has shown that retrieval stopping shares right dorsolateral and ventrolateral prefrontal mechanisms with action stopping, consistent with a domain-general inhibitory control mechanism, but also recruits a distinct fronto-temporal pathway that determines the success of mental control. As part of this pathway, GABAergic inhibition within the hippocampus influences the efficacy of prefrontal control over thought. These unique elements of mental control suggest that hippocampal disinhibition is a transdiagnostic factor underlying intrusive thinking, linking the fronto-temporal control pathway to preclinical models of psychiatric disorders and fear extinction. We suggest that retrieval-stopping deficits may underlie the intrusive thinking that is common across many psychiatric disorders.

Episodic memory, our ability to recall past experiences, is supported by structures in the medial temporal lobe (MTL) particularly the hippocampus, and its interactions with fronto-parietal brain regions. Understanding how these brain regions coordinate to encode, consolidate, and retrieve episodic memories remains a fundamental question in cognitive neuroscience. Non-invasive brain stimulation (NIBS) methods, especially transcranial magnetic stimulation (TMS), have advanced episodic memory research beyond traditional lesion studies and neuroimaging by enabling causal investigations through targeted magnetic stimulation to specific brain regions. This review begins by delineating the evolving understanding of episodic memory from both psychological and neurobiological perspectives and discusses the brain networks supporting episodic memory processes. Then, we review studies that employed TMS to modulate episodic memory, with the aim of identifying potential cortical regions that could be used as stimulation sites to modulate episodic memory networks. We conclude with the implications and prospects of using NIBS to understand episodic memory mechanisms.

The act of recalling memories can paradoxically lead to the forgetting of other associated memories, a phenomenon known as retrieval-induced forgetting (RIF). Inhibitory control mechanisms, primarily mediated by the prefrontal cortex, are thought to contribute to RIF. In this study, we examined whether stimulating the medial prefrontal cortex (mPFC) with transcranial direct current stimulation modulates RIF and investigated the associated electrophysiological correlates. In a randomized study, 50 participants (27 males and 23 females) received either real or sham stimulation before performing retrieval practice on target memories. After retrieval practice, a final memory test to assess RIF was administered. We found that stimulation selectively increased the retrieval accuracy of competing memories, thereby decreasing RIF, while the retrieval accuracy of target memories remained unchanged. The reduction in RIF was associated with a more pronounced beta desynchronization within the left dorsolateral prefrontal cortex (left-DLPFC), in an early time window ($<$500\>ms) after cue onset during retrieval practice. This led to a stronger beta desynchronization within the parietal cortex in a later time window, an established marker for successful memory retrieval. Together, our results establish the causal involvement of the mPFC in actively suppressing competing memories and demonstrate that while forgetting arises as a consequence of retrieving specific memories, these two processes are functionally independent. Our findings suggest that stimulation potentially disrupted inhibitory control processes, as evidenced by reduced RIF and stronger beta desynchronization in fronto-parietal brain regions during memory retrieval, although further research is needed to elucidate the specific mechanisms underlying this effect.

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.

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.

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.

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.

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.

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.

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.

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.

Evidence suggests that theta oscillations recruit distributed cortical representations to improve associative encoding under semantically congruent conditions. Here we show that positive effects of semantic context on encoding and retrieval of associations are mediated by changes in the coupling pattern between EEG theta sources. During successful encoding of semantically congruent face-location associations, the right superior parietal lobe showed enhanced theta phase synchronization with other regions within the lateral posterior parietal lobe (PPL) and left medial temporal lobe (MTL). However, functional coordination involving the inferior parietal lobe was higher in the incongruent condition. These results suggest a differential engagement of top-down and bottom-up mechanisms during encoding of semantically congruent and incongruent episodic associations, respectively. Although retrieval processes operated on a similar neural network, the main difference with the study phase was the larger amount of functional links shown by the lateral prefrontal cortex with regions of the MTL and PPL. All together, these results suggest that theta oscillations mediate, at least partially, the positive effect of semantic congruence on associative memory by (i) optimizing top-down attentional mechanisms through enhanced theta phase synchronization between dorsal regions of the PPL and MTL and (ii) by adjusting the control of automatic attention to sensory and contextual information reactivated in the MTL through functional connections with the inferior parietal lobe during both encoding and retrieval processes.

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.