For whom the bell tolls : periodic reactivation of sensory cortex in the gamma band as a substrate of visual working memory maintenance

Peer reviewedPublisher PD

EEG oscillations reveal neural correlates of evidence accumulation

Recent studies have begun to elucidate the neural correlates of evidence accumulation in perceptual decision making, but few of them have used a combined modeling-electrophysiological approach to studying evidence accumulation. We introduce a multivariate approach to EEG analysis with which we can perform a comprehensive search for the neural correlate of dynamics predicted by accumulator models. We show that the dynamics of evidence accumulation are most strongly correlated with ramping of oscillatory power in the 4–9 Hz theta band over the course of a trial, although it also correlates with oscillatory power in other frequency bands. The rate of power decrease in the theta band correlates with individual differences in the parameters of drift diffusion models fitted to individuals’ behavioral data

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

The role of similarity in memory and its relation to brain oscillations

Similarity is known to affect memory. Visual item recognition refers to tasks where participants study a set of visual stimuli, and have to determine whether a probe item matches one of the items in the study set. This task is naturally sensitive to similarity effects and can well be described using summed similarity models. These models posit that a participant will endorse a probe item as a target (member of the study set) when the sum of the similarities between each study item and the probe exceeds a decision threshold. Although the use of summed similarity models is well established, they have not yet been related to neural data. In this dissertation, I will focus on the relation between brain oscillations recorded through human electroencephalography (EEG), visual item recognition, and summed similarity models. I will start by discussing the similarities and differences between various methods to study brain oscillations. Then I will show that summed similarity correlates with low-frequency theta (4–9 Hz) activity recorded using scalp EEG. Conversely, proactive interference, an increase in task difficulty due to recent presentations of the same study items, is related to the amplitude of 28–90 Hz gamma oscillations. Scalp EEG suffers from low spatial resolution. I obtained intracranial EEG from epilepsy patients suffering from pharmacologically intractable epilepsy, who had electrodes implanted for clinical purposes. I will show that intracranially recorded oscillations also show correlations with summed similarity, and differentiate between probe-item similarity and item-item similarity in the medial temporal lobe. I will also show that with memory load, high-frequency oscillatory power in the medial temporal lobe increases and low-frequency oscillatory power in parietal and perceptual areas decreases. Together, these findings show first, that similarity plays an important role in human memory. Second, summed similarity models can be directly related to oscillatory brain activity, particularly in the theta band. This opens up directions for future research, relating mathematical models of cognition to neural activity

The role of similarity in memory and its relation to brain oscillations

Similarity is known to affect memory. Visual item recognition refers to tasks where participants study a set of visual stimuli, and have to determine whether a probe item matches one of the items in the study set. This task is naturally sensitive to similarity effects and can well be described using summed similarity models. These models posit that a participant will endorse a probe item as a target (member of the study set) when the sum of the similarities between each study item and the probe exceeds a decision threshold. Although the use of summed similarity models is well established, they have not yet been related to neural data. In this dissertation, I will focus on the relation between brain oscillations recorded through human electroencephalography (EEG), visual item recognition, and summed similarity models. I will start by discussing the similarities and differences between various methods to study brain oscillations. Then I will show that summed similarity correlates with low-frequency theta (4–9 Hz) activity recorded using scalp EEG. Conversely, proactive interference, an increase in task difficulty due to recent presentations of the same study items, is related to the amplitude of 28–90 Hz gamma oscillations. Scalp EEG suffers from low spatial resolution. I obtained intracranial EEG from epilepsy patients suffering from pharmacologically intractable epilepsy, who had electrodes implanted for clinical purposes. I will show that intracranially recorded oscillations also show correlations with summed similarity, and differentiate between probe-item similarity and item-item similarity in the medial temporal lobe. I will also show that with memory load, high-frequency oscillatory power in the medial temporal lobe increases and low-frequency oscillatory power in parietal and perceptual areas decreases. Together, these findings show first, that similarity plays an important role in human memory. Second, summed similarity models can be directly related to oscillatory brain activity, particularly in the theta band. This opens up directions for future research, relating mathematical models of cognition to neural activity