Hamid Aghajan

Information processing is a core element of cognitive processes in the brain. Rooted in the idea postulated by John von Neumann that the stochasticity of neural operations might essentially be a reflection of the probabilistic nature of information, it can be argued that fluctuations in the complexity of the brain operations should reflect aspects of information processing activities undertaken to infer nuances embedded in the sensory data. The current study elucidates these aspects by examining the intricate relationship between information processing in the brain, reflected in the complexity of brain activities, and information-theoretical measures of uncertainty in the Bayesian models of sequence learning. A key finding of our study is that the brain complexity shows a significantly superior correlation with the uncertainty about the input, captured by the entropy of the posterior distribution over all input values, compared to its correlation with the novel information stored in the current input as measured by the Shannon or Bayesian models of surprise. Additionally, we found that the brain complexity follows the habituation process known to occur during learning by demonstrating a decreasing trend and also corroborates the process of spontaneous recovery at the onset of a repeated learning task. As further evidence for this relationship, distinguished complexity trends are observed in patients of Parkinson disease, reflecting cognitive disorders in habituation and habit formation caused by the disease.

Dementia, and in particular Alzheimer’s disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain’s default mode network (DMN). The DMN operation is known to be impacted by dementia’s progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity.

Background: Flickering light stimulation has been shown to entrain the brain’s oscillatory activity at the input frequency and synchronize different cortical regions in that frequency (Herrmann et al., 2001; Lahijanian et al., 2021). Brain entrainment at gamma frequency has been proposed and shown promising results as a non‐pharmaceutical therapeutic approach for Alzheimer’s disease (Cimenser et al., 2021). Studying the effects of such stimulation on the brain’s neural activity can help explain the mechanisms that underlie the reported improvements. It is known that neural oscillations form traveling waves across the brain during various cognitive functions (Bhattacharya et al., 2022). In this study, we use different flickering light frequencies as entrainment input and investigate the spatiotemporal pattern of neural activity modulated by visual entrainment which appears as a traveling wave across the posterior‐frontal pathway.Method: EEG data recorded from young healthy adults were analyzed during multi‐trial flickering light stimulation at 10, 15, 20, 22, and 30Hz frequencies. In order to analyze the quality of posterior‐frontal waves, the spatiotemporal phase pattern was measured over the head midline. The wave quality was defined as the consistency of the spatiotemporal phase pattern during consecutive time intervals, and the quality of entrainment was defined as the intensity of the power spectral density at the input frequency.Result: During the resting state, different wave patterns traveling across the frontoparietal link are canceled out. However, visual stimulation invigorates the forward wave from the sensory (posterior – visual) region to the upstream area (frontal lobe) at the target frequency compared to the baseline. Meanwhile, in contrast to the forward wave, traveling wave in the backward direction does not show noticeable presence at the stimulation frequency. Furthermore, as the entrainment quality increases, the wave pattern consistency is improved for all stimulating frequencies (Figs. 1, 2).Conclusion: The forward wave induced during non‐invasive visual stimulation represents long‐range activity along the posterior‐frontal pathway known to be activated during sensory processing and inference. The observed correlation between the wave’s quality and the quality of entrainment at the target frequency points to the mechanism underlying the therapeutic effects of gamma entrainment.

The motor symptoms of Parkinson's disease (PD) have been shown to significantly improve by Levodopa. However, despite the widespread adoption of Levodopa as a standard pharmaceutical drug for the treatment of PD, cognitive impairments linked to PD do not show visible improvement with Levodopa treatment. Furthermore, the neuronal and network mechanisms behind the PD-induced cognitive impairments are not clearly understood. In this work, we aim to explain these cognitive impairments, as well as the ones exacerbated by Levodopa, through examining the differential dynamic patterns of the phase-amplitude coupling (PAC) during cognitive functions. EEG data recorded in an auditory oddball task performed by a cohort consisting of controls and a group of PD patients during both on and off periods of Levodopa treatment were analyzed to derive the temporal dynamics of the PAC across the brain. We observed distinguishing patterns in the PAC dynamics, as an indicator of information binding, which can explain the slower cognitive processing associated with PD in the form of a latency in the PAC peak time. Thus, considering the high-level connections between the hippocampus, the posterior and prefrontal cortices established through the dorsal and ventral striatum acting as a modulatory system, we posit that the primary issue with cognitive impairments of PD, as well as Levodopa’s cognitive deficit side effects, can be attributed to the changes in temporal dynamics of dopamine release influencing the modulatory function of the striatum.

Background: Theta rhythms are known to encode sensory information and reflect their effect into gamma oscillations through a modulation process known as theta‐gamma phase‐amplitude coupling (PAC) (Amemiya et al., Cell Reports 2018). The role of the PAC as a network‐level indicator of cognitive impairment in disorders such as dementia and Alzheimer’s disease has received much attention recently (Zheng et al. Nature communication 2020).Injection of Streptozotocin (STZ) in animals is reported to cause resistance to insulin uptake in the brain, leading to memory and cognitive impairment (Kamat, Neural Regeneration Research 2015). The current study examines the induced deficit in the PAC as an early indicator of neurodegenerative effects of STZ.Method: 3 adult male Wistar rats were included in this study (270 – 300 g). Intraventricular injection of STZ was performed at a dose of 3 mg / Kg with a volume of 10 μl. Two stainless steel electrodes were implanted in the hippocampus (CA1) and medial prefrontal cortex (mPFC). The rats were free to move during recording. LFP data were recorded and segmented into windows of 20 s. Preprocessing and noisy trial rejection were employed for artifact removal, and the theta‐gamma PAC was measured using the mean vector length (tf‐MVL) method (Munia et al., Scientific Reports 2019).Result: One week after STZ injection, the theta‐gamma PAC values did not show a significant change but after two weeks variation was observed. During this period, the power of different frequency bands was also measured and no significant change was observed even after two weeks. Theta‐gamma PAC deficiency was observed in both regions (CA1 and mPFC).Conclusion: STZ’s intracerebro‐ventricular injection is known to produce cognitive deficits along with an increase in Aβ deposition. This study reports that cross‐frequency coupling deficiency is also observed in this AD mouse model. Moreover, the PAC deficit was detected earlier than any change was observed in the spectral analysis of the data, suggesting that it can serve as a diagnosis tool for memory impairment due to neurodegeneration in the STZ mouse model of AD.

Mild cognitive impairment (MCI) is known as an early stage of cognitive decline. Amnestic MCI (aMCI) is considered as the preliminary stage of dementia which may progress to Alzheimer’s disease (AD). While some aMCI patients may stay in this condition for years, others might develop dementia associated with AD. Early detection of MCI allows for potential treatments to prevent or decelerate the process of developing dementia. Standard methods of diagnosing MCI and AD employ structural (imaging), behavioral (cognitive tests), and genetic or molecular (blood or CSF tests) techniques. Our study proposes network-level neural synchronization parameters as topographical markers for diagnosing aMCI and AD. We conducted a pilot study based on EEG data recorded during an olfactory task from a group of elderly participants consisting of healthy individuals and patients of aMCI and AD to assess the value of different indicators of network-level phase and amplitude synchronization in differentiating the three groups. Significant differences were observed in the percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups, and classifiers were developed to differentiate the three groups based on these parameters. The observed differences in these indicators of network-level functionality of the brain can help explain the underlying processes involved in aMCI and AD.

Surprise and social influence are linked through several neuropsychological mechanisms. By garnering attention, causing arousal, and motivating engagement, surprise provides a context for effective or durable social influence. Attention to a surprising event motivates the formation of an explanation or updating of models, while high arousal experiences due to surprise promote memory formation. They both encourage engagement with the surprising event through efforts aimed at understanding the situation. By affecting the behavior of the individual or a social group via setting an attractive engagement context, surprise plays an important role in shaping personal and social change. Surprise is an outcome of the brain’s function in constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from the brain’s predictions shaped by recent trends, distinct neural signals are generated to report this surprise. As a quantitative approach to modeling the generation of brain surprise, input stimuli containing surprising elements are employed in experiments such as oddball tasks during which brain activity is recorded. Although surprise has been well characterized in many studies, an information-theoretical model to describe and predict the surprise level of an external stimulus in the recorded MEG data has not been reported to date, and setting forth such a model is the main objective of this paper. Through mining trial-by-trial MEG data in an oddball task according to theoretical definitions of surprise, the proposed surprise decoding model employs the entire epoch of the brain response to a stimulus to measure surprise and assesses which collection of temporal/spatial components in the recorded data can provide optimal power for describing the brain’s surprise. We considered three different theoretical formulations for surprise assuming the brain acts as an ideal observer that calculates transition probabilities to estimate the generative distribution of the input. We found that middle temporal components and the right and left fronto-central regions offer the strongest power for decoding surprise. Our findings provide a practical and rigorous method for measuring the brain’s surprise, which can be employed in conjunction with behavioral data to evaluate the interactive and social effects of surprising events.