EEG subject-dependent neurofeedback training selectively impairs declarative memories consolidation process.

The process of stabilization and storage of memories, known as consolidation, can be modulated by different interventions. Research has shown that self-regulation of brain activity through Neurofeedback (NFB) during the consolidation phase significantly impacts memory stabilization. While some studies have successfully modulated the consolidation phase using traditional EEG-based Neurofeedback (NFB) that focuses on general parameters, such as training a specific frequency band at particular electrodes, they often overlook the unique and complex neurodynamics that underlie each memory content in different individuals, potentially limiting the selective modulation of memories. The main objective of this study is to investigate the effects of a Subject-Dependent NFB (SD-NFB), based on individual models created from the brain activity of each participant, on long-term declarative memories. Participants underwent an experimental protocol involving three sessions. In the first session, they learned images of faces and houses while their brain activity was recorded. This EEG data was used to create individualized models to identify brain patterns related to learning these images. Participants were then divided into three groups, with one group receiving SD-NFB to enhance brain activity linked to faces, another to houses, and a CONTROL sham group that did not receive SD-NFB. Memory performance was evaluated 24 h and seven days later using an 'old-new' recognition task, where participants distinguished between 'old' and 'new' images. The results showed that memory contents (faces or houses) whose brain patterns were trained via SD-NFB scored lower in recognition compared to untrained contents, as evidenced 24 h and seven days post-training. In summary, this study demonstrates that SD-NFB can selectively impact the consolidation of specific declarative memories. This technique could hold significant implications for clinical applications, potentially aiding in the modulation of declarative memory strength in neuropsychiatric disorders where memories are pathologically exacerbated.

Classifying brain states and pupillary responses associated with the processing of old and new information.

Memory retrieval of consolidated memories has been extensively studied using "old-new tasks", meaning tasks in which participants are instructed to discriminate between stimuli they have experienced before and new ones. Significant differences in the neural processing of old and new elements have been demonstrated using different techniques, such as electroencephalography and pupillometry. In this work, using the data from a previously published study (Campos-Arteaga, Forcato et al. 2020), we investigated whether machine learning methods can classify, based on single trials, the brain activity and pupil responses associated with the processing of old and new information. Specifically, we used the EEG and pupillary information of 39 participants who completed an associative recall old-new task in which they had to discriminate between previously seen or new pictures and, for the old ones, to recall an associated word. Our analyses corroborated the differences in neural processing of old and new items reported in previous studies. Based on these results, we hypothesized that the application of machine learning methods would allow an optimal classification of old and new conditions. Using a Windowed Means approach (WM) and two different machine learning algorithms - Logistic Regression (WM-LR) and Linear Discriminant Analysis (WM-LDA) - mean classification performances of 0.75 and 0.74 (AUC) were achieved when EEG and pupillary signals were combined to train the models, respectively. In both cases, when the EEG and pupillary data were merged, the performance was significantly better than when they were used separately. In addition, our results showed similar classification performances when fused classification models (i.e., models created with the concatenated information of 38 participants) were applied to individuals whose EEG and pupillary information was not considered for the model training. Similar results were found when alternative preprocessing methods were used. Taken together, these findings show that it is possible to classify the neurophysiological activity associated with the processing of experienced and new stimuli using machine learning techniques. Future research is needed to determine how this knowledge might have potential implications for memory research and clinical practice.

Differential neurophysiological correlates of retrieval of consolidated and reconsolidated memories in humans: An ERP and pupillometry study.

Consolidated memories can return to a labile state if they are reactivated by unpredictable reminders. To persist, active memories must be re-stabilized through a process known as reconsolidation. Although there is consistent behavioral evidence about this process in humans, the retrieval process of reconsolidated memories remains poorly understood. In this context, one fundamental question is whether the same or different neurophysiological mechanisms are involved in retrieval of consolidated and reconsolidated memories. Because it has been demonstrated that the exposure to the reconsolidation process may restructure and strengthen memories, we hypothesized distinct neurophysiological patterns during retrieval of reconsolidated memories. In addition, we hypothesized that interfering with the reconsolidation process using a new learning can prevent these neurophysiological changes. To test it, consolidated, reconsolidated and declarative memories whose reconsolidation process was interfered (i.e., picture-word pairs) were evaluated in humans in an old/new associative recall task while the brain activity and the pupillary response were recorded using electroencephalography and eyetracking. Our results showed that retrieval of reconsolidated memories elicits specific patterns of brain activation, characterized by an earlier peak latency and a smaller magnitude of the left parietal ERP old/new effect compared to memories that were only consolidated or whose reconsolidation process was interfered by a new learning. Moreover, our results demonstrated that only retrieval of reconsolidated memories is associated with a late reversed mid-frontal effect in a 600-690 time window. Complementarily, memories that were reactivated showed an earlier peak latency of the pupil old/new effect compared to non-reactivated memories. These findings support the idea that reconsolidation has an important impact in how memories are retrieved in the future, showing that retrieval of reconsolidated memories is partially supported by specific brain mechanisms.