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Extended performance of cognitively demanding tasks induces cognitive fatigue manifested with an overall deterioration of behavioral performance. In particular, long practice with tasks requiring impulse control is typically followed by a decrease in self-control efficiency, leading to performance instability. Here, we show that this is due to changes in activation modalities of key task-related areas occurring if these areas previously underwent intensive use. We investigated in 25 healthy adults the effects of extended practice with high cognitive demand (HCD) tasks on a Go-No Go task and the underlying electroencephalographic (EEG) activity. We compared these effects with those induced by practice with similar, but low cognitive demand (LCD) tasks. HCD tasks were followed by an increase in response inhibition failures. These were correlated with the appearance of a distinct neural signature on fast response trials, characterized by lower levels of beta ([13-30] Hz) EEG activity in the pre-stimulus period, and by a lack of EEG markers of pre-response processing in frontal areas. Moreover, HCD tasks were followed by a decrease in N200 during correct withholds while LCD tasks were followed instead by a lesser fraction of hits and a decrease in P300, suggesting a decrease in engagement. Overall, these results show that exertion of cognitive control determines the appearance of two distinct modalities of response with different processing speeds, associated with distinct underlying neural activity. Significance statement Extended cognitive load leads to alterations in behavior, but the underlying alterations in cortical activity are far from being understood. When we compared the performance in a Go/NoGo test before and after a battery of tasks requiring high cognitive control, we found an increase in commission errors associated with an increase in fast automatic responses. EEG signals of these responses displayed a lack of cortical markers of pre-response processing. Tasks requiring only low cognitive control were followed instead by an increase in miss errors, likely related to a decrease in engagement. Extended cognitive load leads then to the appearance of two distinct response modalities, driven by distinct neural activities., (Copyright © 2025 Taddeini et al.)

Competing Interests: Declarations. Conflicts of interest: The author declares no conflicts of interest. Ethical approval: This study was performed in accordance with ethical standards.

In 1924, at the University Hospital of Jena, Hans Berger first recorded an electrical brain signal in humans. This discovery revolutionized clinical neuroscience and neurotechnology, as it contributed to both electrophysiology and the development of the electroencephalogram (EEG). This article provides a historical overview of Hans Berger's seminal contributions, highlighting the importance of his early recordings, the motivations that drove him, and the scientific problems he had to initiate and solve, in a historical context of profoundly changing circumstances. He also faced low acceptance of his works initially, and only belatedly did they become accepted by the scientific community. Berger was known to be a humble but tenacious person who believed in his convictions to the core, and this strength of will is an example of passion for students and scholars of neuroscience. NEW & NOTEWORTHY In 1924, at the University Hospital in Jena, Hans Berger first recorded human brain electrical signals, revolutionizing clinical neurophysiology and neurotechnology. He developed the electroencephalogram (EEG) and identified alpha waves in the human scalp. Although initially met with skepticism, his work was later recognized as fundamental. Berger's perseverance and conviction in his research serve as an inspiring example of dedication for students and scientists in neuroscience.

Impulsive-compulsive behaviors (ICB) are common non-motor symptoms of Parkinson's disease (PD) often associated with dopaminergic drugs (DD) therapy. We investigated the acute effects of DD on decision-making in PD patients with ICB (ICB+) and without it (ICB-), and in healthy controls (HC). Participants performed a risk-based decision-making task twice, with PD patients tested before (DD OFF) and after (DD ON) DD intake. In DD OFF, all groups developed a risk-averting strategy. In DD ON, ICB+ patients (but not ICB- nor HC) reverted to riskier choices. We conclude that DD has a specific strong acute effect on ICB+ patients' decision-making., (© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Introduction: Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern., Methods: We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n  = 17), subjective cognitive decline (SCD, n  = 58), and mild cognitive impairment (MCI, n  = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings., Results: Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50)., Discussion: Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings., Highlights: Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y., Competing Interests: The authors have no relevant conflicts of interest to declare. Author disclosures are available in the supporting information., (© 2024 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia., Methods: We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ 42 , t-tau, and p-tau concentration and Aβ 42 /Aβ 40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD., Discussion: This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD., Trial Registration Number (trn): NCT05569083., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Motor symptoms of Parkinson's Disease (PD) are associated with dopamine deficits and pathological oscillation of basal ganglia (BG) neurons in the β range ([12-30] Hz). However, how dopamine depletion affects the oscillation dynamics of BG nuclei is still unclear. With a spiking neurons model, we here capture the features of BG nuclei interactions leading to oscillations in dopamine-depleted condition. We highlight that both the loop between subthalamic nucleus (STN) and Globus Pallidus pars externa (GPe) and the loop between striatal fast spiking and medium spiny neurons and GPe display resonances in the β range, and synchronize to a common β frequency through interaction. Crucially, the synchronization depends on dopamine depletion: the two loops are largely independent for high levels of dopamine, but progressively synchronize as dopamine is depleted due to the increased strength of the striatal loop. The model is validated against recent experimental reports on the role of cortical inputs, STN and GPe activity in the generation of β oscillations. Our results highlight the role of the interplay between the GPe-STN and the GPe-striatum loop in generating sustained β oscillations in PD subjects, and explain how this interplay depends on the level of dopamine. This paves the way to the design of therapies specifically addressing the onset of pathological β oscillations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ortone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; however, these bumps can be merged by overcoming the repeller dynamics. A simple associative memory can include these bump attractors, allowing the use of continuous variables in these memories, and these associations can be learned by Hebbian rules. These simulations are related to biological networks, showing that this is a step toward a more complete neural cognitive associative memory.