Neural Activity Research Articles (Page 1)

Motor symptoms of Parkinson's disease (PD) are associated with dopaminergic neuronal loss. Widespread synaptic reorganization and neural activity changes, including exaggerated beta oscillations and bursting, follow dopamine depletion (DD) of the basal ganglia (BG). Our computational model examines DD-induced neural activity changes and synaptic reorganization in the BG subcircuit comprising the subthalamic nucleus and globus pallidus externus. Calcium-dependent synaptic and structural plasticity mechanisms were incorporated, allowing neural activity to alter network topology. We show how hyperactivity of indirect pathway striatal projection neurons (iMSN) can induce synaptic connectivity changes consistent with PD animal models. Our results suggest that synaptic reorganization following DD results from a series of homeostatic calcium-based synaptic changes triggered by iMSN hyperactivity. While this structural plasticity functions as a compensatory mechanism in the cascade of changes following elevated iMSN input from striatal DD, it may become compromised if iMSN and cortical inputs show substantial bursting activity.

Sustained attention, the ability to focus on a stimulus or task over extended periods, is crucial for higher level cognition, and is impaired across multiple neuropsychiatric and neurodevelopmental disorders, including attention-deficit/hyperactivity disorder, schizophrenia, and depression. The rodent continuous performance test (rCPT) is a translational task that can be used to investigate the cellular mechanisms underlying sustained attention. Electrophysiological single unit and local field potential (LFPs) recordings reflect changes in neural activity in the prelimbic cortex (PrL) in mice performing sustained attention tasks. While evidence linking PrL neuronal activity to sustained attention is compelling, most studies have focused on single-cell activity surrounding behavioral responses, overlooking population-level dynamics across entire sessions that could offer additional insight into fluctuations in attention during task performance. Here, we used in vivo endoscopic calcium imaging to record patterns of neuronal activity in PrL neurons using the genetically encoded calcium sensor GCaMP6f in mice performing the rCPT at three timepoints characterized by differing levels of cognitive demand and task proficiency. We analyzed single-cell activity surrounding behavioral responses and population-level dynamics across sessions to determine how PrL neuronal activity evolves with sustained attention performance. A higher proportion of PrL neurons were recruited during correct responses in sessions requiring high task proficiency. Moreover, during rCPT sessions, mice intercalated responsive-engaged periods with non-responsive-disengaged periods. Applying a Hidden Markov Model (HMM) with two states to global PrL activity, we found neuronal states associated with task engagement. These states are characterized by different levels of correlated neuronal activity within PrL neurons. Overall, these findings illustrate that task proficiency, and task engagement differentially recruit activity in PrL neurons during a sustained attention task.

Structure in noise: Recurrent connectivity shapes neural variability to balance perceptual and cognitive demands in the human brain.

Stress-related neural activity (SNA), as measured by amygdala metabolism, has been linked in prior work to all-cause mortality and major adverse cardiovascular events. In this study, we sought to clarify SNA determinants and test whether age modifies its association with all-cause mortality. Using 2-[18 F]fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG-PET), we quantified amygdala metabolism, a surrogate for SNA, in 1,336 patients (mean age 59.4 ± 15.6 years, 37.8% women). Assessing demographic and imaging confounders, associations between SNA and mortality were evaluated in a subgroup of 960 participants with a median 5-year follow-up (IQR 3-9). Higher SNA appears independently associated with greater all-cause mortality across all age groups (HR 1.45, 95% CI 1.08-1.95; p = 0.012). The association is strongest in younger, healthier individuals (HR 7.86, 95% CI 2.92-21.21; p < 0.001) and attenuates with advancing age. Mediation analysis indicates that SNA accounts for 38.2% (95%CI 15.7%-60.7%) of the age-mortality link. Here, we find that SNA is independently associated with all-cause mortality, with effect sizes that diminish with age; if confirmed, incorporating SNA into risk models alongside conventional factors may improve mortality prediction and help identify younger adults, who appear low risk by standard criteria, for closer follow-up and preventive strategies.

Flexible goal-directed behavior relies on selecting relevant internal goal representations and external sensations. Yet, these selection processes have classically been studied in isolation, leaving unclear how they are coordinated in time to support behavior. To address this, we developed a visual search task to simultaneously track selection among internal search goals held in working memory and external search targets in the environment. Capitalizing on sensitive gaze and neural markers, we provide proof-of-principle evidence in humans that internal and external selection processes do not necessarily take turns in a strictly serial manner but can develop concurrently. These concurrent processes are supported by largely nonoverlapping neural activity patterns in the human brain and can be performed effectively even when engaging opposite spatial locations in working memory and perception. Our findings challenge views portraying brain states as being either internally or externally focused and bring insight into how internal and external selection processes work together to yield efficient search behavior.

Research indicates that midbrain dopaminergic neurons encode reward prediction error (RPE) signals involved in positive reinforcement learning. However, studies on dopamine's role in negative reinforcement learning (NRL) are scarce. Learning to escape aversive stimuli is vital for survival and may differ significantly from positive reinforcement in behavior and neural mechanisms. This study employs footshocks as aversive stimuli to investigate neural activity, synaptic transmission, and intrinsic excitability in a NRL paradigm using fiber photometry and ex vivo electrophysiology. Results show that inescapable footshocks initially increase activity in substantia nigra pars compacta (SNc) dopaminergic neurons, which later shifts to reflect shock termination as exposure increases. Electrophysiological observations reveal increased intrinsic excitability and excitatory synaptic transmission in SNc neurons, with decreased inhibitory transmission. After mice learn to escape the shock by nose-poking, dopaminergic activity shifts from shock termination to shock onset. Furthermore, inhibitory input increases, while excitatory input decreases after learning, with intrinsic excitability returning to baseline levels. This indicates that SNc dopaminergic neurons exhibit RPE-like signals in response to aversive stimuli, with their intrinsic excitability adjusting according to expectations of shock termination. These findings enhance our understanding of RPE encoding in negative reinforcement learning and may inform therapeutic strategies for disorders caused by environmental factors such as aversive stimuli.

When and how motor cortical output directly influences limb muscle activity through descending projections remain poorly resolved, impeding a mechanistic understanding of motor control. Here we addressed this in mice performing an ethologically inspired climbing behavior. We quantified the direct influence of forelimb primary motor cortex (caudal forelimb area) on muscles across the muscle activity states expressed during climbing. We found that the caudal forelimb area instructs muscle activity pattern by selectively activating certain muscles, while less frequently activating or suppressing their antagonists. From Neuropixels recordings, we identified linear combinations (components) of motor cortical activity that covary with these effects. These components differ partially from those that covary with muscle activity and differ almost completely from those that covary with kinematics. Collectively, our results reveal an instructive direct motor cortical influence on limb muscles that is selective within a motor behavior and reliant on a distinct neural activity subspace.

Examining the cognitive effects of digital technology use in young adults will contribute to scientific knowledge and guide education and health. This scoping review aims to evaluate the cognitive effects of digital technology use in young adults as a result of the search conducted in DergiPark, Science Direct, Wiley Online Library, Medline Plus, DOAJ, PubMed, EBSCO, Scopus, Web of Science, IEEExplore, and ACM Digital Library databases without any time limitation. Twelve studies that meet the criteria were included in the review by the researchers. The included studies examined the cognitive effects of digital technology use in terms of memory, learning, and information processing in the included studies. Most studies showed that digital technology use creates adverse cognitive effects such as low recall accuracy, decreased neural activity in brain regions associated with memory, comprehension and transcribing issues about information, self-confidence problems during answering recall tests, forgetfulness about future events, and misinterpretation of the remembering process. According to the research findings, addiction to digital technology, trust in the process of recording information, cognitive effort shown in accessing and using information, and different methods used in recording information were determined as factors affecting the emergence of adverse cognitive effects. These results indicate that methodologically strong and comprehensive studies are necessary to understand the cognitive effects of digital technology use.

Prior research has demonstrated a link between perceived discrimination during adolescence and depression symptoms, but few studies have examined whether this link holds into young adulthood. Moreover, little is known about the neural mechanisms that explain the association between discrimination and depression, or the protective cultural factors that buffer youth against the adverse effects of discrimination. These gaps were addressed with a sample of 196 Mexican-origin youth (51% female) enrolled in the longitudinal California Families Project. Perceived discrimination was assessed in early adolescence (ages 10-14); participants completed two neuroimaging scans while experiencing social exclusion in late adolescence (ages 16 and 18); and, depression symptoms were assessed in young adulthood (ages 21 and 23). Growth curve analyses tested whether perceived discrimination was associated with depression symptoms in young adulthood, whether exclusion-related neural activity mediated this association, and whether cultural factors (ethnic pride, familism) moderated these associations. No significant effects were found, except that higher levels of ethnic pride in later adolescence were associated with lower levels of depression symptoms in young adulthood (β = - .17, SE = .06, p = 0.007). Findings suggest that ethnic pride could be leveraged in interventions to promote more positive mental health outcomes in Mexican-origin individuals in young adulthood.

Blindness is one of the most impactful disabilities in human lives. Cortical prostheses could one day restore functional vision in some blind subjects, but their success will depend on integrating advanced technologies to realize the therapeutic benefits they promise. Most previous studies in humans used electrodes only for stimulation, which has made it challenging to precisely control the appearance of individual phosphenes. Herein, we implanted an intracortical microelectrode array of 100 electrodes in the visual cortex of two blind volunteers. We recorded the neural activity around the electrodes while performing electrical stimulation to induce visual perceptions. Besides showing how stimulation parameters influence perceptual thresholds, perceived brightness, and the minimum interval required to distinguish separate stimuli, our results indicate that subjective visual experience can be accurately predicted from the recorded neural activity. These results highlight the potential for using the neural activity of neighboring electrodes to accurately infer and control visual perceptions in cortical visual prostheses.

Amyloid-β (Aβ) is recognized as a pathological hallmark of Alzheimer's disease, but accumulating evidence suggests that it also serves physiological roles in the healthy brain. Notably, Aβ secretion is tightly linked to neuronal activity and wakefulness, and its clearance is facilitated by sleep, raising the possibility that Aβ regulates sleep homeostasis. We propose that Aβ functions as a cytokine-like somnogen: a molecule whose accumulation during wakefulness promotes sleep onset and maintenance via synaptic and immune mechanisms. This framework reframes Aβ not as a toxic byproduct but as a key intermediary between neural activity and restorative sleep processes. We synthesize findings from molecular biology, electrophysiology, animal models, and human sleep studies, including research on AβPP processing, activity-dependent Aβ release, oligomeric signaling, and the effects of anti-amyloid therapies on sleep. Particular emphasis is placed on evidence that Aβ modulates synaptic excitability, engages glial immune pathways, and fulfills formal criteria for cytokine classification. Across multiple systems, Aβ exhibits properties consistent with homeostatic downscaling: it dampens neurotransmitter release, suppresses excitatory receptor trafficking, and activates sleep-promoting neuronal populations. Disruption of endogenous AβPP cleavage impairs sleep consolidation, while depletion of Aβ can lead to network hyperexcitability and disturbed sleep. Post-marketing reports of insomnia and abnormal dreams with plaque-clearing agents further support a physiological role. Recognizing Aβ as a somnogen offers a unifying model for sleep disruption in AD and raises caution about therapies that neutralize Aβ indiscriminately. Future interventions may benefit from preserving Aβ's homeostatic roles while mitigating its pathological aggregation.

Mouse superficial superior colliculus (sSC) has been found to have orientation selective maps, suggesting a fundamentally different selectivity than in primate SC. Moreover, orientation selectivity in mouse sSC appears to change with stimulus properties such as size, shape and spatial frequency, in contradistinction to the computational principle of invariance in primates. To reconcile mouse and primate mechanisms for orientation selectivity, we constructed a computational model of mouse sSC populations with circular-symmetric, center-surround (i.e., not intrinsically orientation selective), stimulus-invariant receptive fields (RFs), classically used to describe monkey lateral geniculate nucleus (LGN) neurons. This model produced population maps similar to those found in mouse sSC, which show strong radial orientation preferences at retinotopic locations along stimulus edges. We show how this selectivity depended critically on spatial frequency tuning of the model units. The model predicted a shift from radial to anti-radial orientation preferences from the same simulated units at high stimulus spatial frequencies, also consistent with measurements from mouse sSC. We found intrinsically oriented RFs were largely unnecessary to explain the imaging data, but could explain a possible small subpopulation of intrinsically orientation selective neurons. We conclude that to study orientation selectivity in mouse sSC and other systems, the problem is not the choice of stimulus. Rather than endless tweaks to find the perfect, unbiased stimulus, image-computable population modeling is the solution. Regardless of the stimulus presented, comparing how well models of intrinsically or non-intrinsically orientation selective units account for empirical data provides definitive evidence for underlying neural selectivity.Significance Statement Measurements of neural population activity from mouse superior colliculus (SC) show patterns of orientation selectivity differing markedly from those observed in primates. Do such measurements necessarily imply different neural mechanisms across species? We developed a modeling framework that explicitly predicts population activity using well-established mechanisms from classic primate single-unit neurophysiology. Notably, this framework was sufficient to explain a diverse array of population measurements in mouse SC. Our results reconcile seemingly contradictory neural phenomena across species and visual areas through a principled approach for making inferences across measurement scales (i.e., single neurons to neural populations), providing a unifying framework for determining shared computational mechanisms broadly throughout the brain.

The purpose of the study is to investigate human physiological responses to construction noise exposure using deep learning, applying electroencephalography (EEG) and electro-dermal activity (EDA) sensors. Construction noise is a pervasive occupational stressor that affects physiological states and impairs cognitive performance. EEG sensors capture neural activity related to perception and attention, and EDA reflects autonomic arousal and stress. In this study, twenty-five participants were exposed to impulsive noise from pile drivers and tonal noise from earth augers at three intensity levels (40, 60, and 80 dB), while EEG and EDA signals were recorded simultaneously. Convolutional neural networks (CNN) were utilized for EEG and long short-term memory networks (LSTM) for EDA. The results depict that EEG-based models consistently outperformed EDA-based models, establishing EEG as the dominant modality. In addition, decision-level fusion enhanced robustness across evaluation metrics by employing complementary information from EDA sensors. Ablation analyses presented that model performance was sensitive to design choices, with medium EEG windows (6 s), medium EDA windows (5–10 s), smaller batch sizes, and moderate weight decay yielding the most stable results. Further, retraining with ablation-informed hyperparameters confirmed that this configuration improved overall accuracy and maintained stable generalization across folds. The outcome of this study demonstrates the potential of deep learning to capture multimodal physiological responses when subjected to construction noise and emphasizes the critical role of modality-specific design and systematic hyperparameter optimization in achieving reliable annoyance detection.

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments (e.g., all-optical interrogation, closed-loop control) for interrogating neural circuits. Unfortunately, these advances have drastically increased the complexity of designing experiments, with ad hoc decisions often resulting in suboptimal or even failed experiments. Bridging model and experiment via simulation can help solve this problem, leveraging advances in computational models to provide a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, we require an integrated approach that incorporates simulation of the experimental interface into computational models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of virtual recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. Notably, it is the only publicly available tool to date simulating two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, evaluate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.Significance statement The rapid expansion of neuroscience tools offers immense potential but also makes it extremely difficult to select the best tools when planning an experiment. Simulations can help streamline this process, but existing neural simulators lack support for cutting-edge techniques combining optogenetics (controlling neurons with light), electrical recording, microscopy, and closed-loop stimulation. To fill this gap, we developed the opensource software Cleo, which integrates diverse models of these tools and saves researchers the hard work of implementing them ad hoc. Cleo's capabilities are demonstrated through both replication of published experiments and the prototyping of novel ones, providing crucial infrastructure to enhance the synergy between experimentation, modeling, and advanced methods.

Dysphagia is commonly assessed with qualitative and image-based diagnostic tools, which are often costly, technically demanding, and limited in their ability to support individualized rehabilitation. Electroencephalography (EEG) has recently emerged as a quantitative, cost-effective, and accessible alternative to characterize sensorimotor activity during swallowing, though its potential in dysphagic populations has not been systematically explored. This study investigated neural dynamics in 50 post-stroke dysphagic patients, 32 post-stroke non-dysphagic controls, and 21 healthy adults performing a swallowing task. EEG recordings from primary motor regions (C3, Cz, C4) were analyzed using event-related spectral perturbation (ERSP) to quantify alpha (8–13 Hz) and beta (15–30 Hz) event-related desynchronization, alongside hemispheric lateralization indices. Group comparisons revealed significantly reduced beta desynchronization in both post-stroke groups compared to healthy participants, with additional alpha and beta deficits at C3 and Cz distinguishing dysphagic patients from non-dysphagic controls. Dysphagic patients further exhibited abnormal lateralization not observed in other groups. These findings identify distinct alterations in motor cortical dynamics and hemispheric balance in dysphagia, supporting EEG-derived biomarkers as promising tools for diagnosis and clinical follow-up. The accessibility of EEG reinforces its potential integration into routine workflows to enable objective and personalized management of post-stroke dysphagia.

Minimal hepatic encephalopathy (MHE) is the initial stage of hepatic encephalopathy (HE), MHE patients have associated with widespread neuro-psychological impairment. Liver transplantation (LT) can restore metabolic abnormalities but the mechanisms are unclear. This study aimed to longitudinally evaluate brain function alteration in MHE patients one month after LT and their correlation with cognitive changes by using resting-state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI data was collected from 32 healthy controls and 27 MHE before and 1 month after LT. Between-group comparisons of demographic data and neuropsychological scores were analyzed using SPSS 25.0. Functional imaging data were analyzed using RESTplus and SPM12 software based on MATLAB 2017b. Gender, age, and years of education were used as covariates to obtain low-frequency fluctuationd (ALFF) and dynamic low-frequency fluctuation (dALFF) dindices. Correlation analyses were performed to explore the relationship between the change of ALFF and dALFF with the change of clinical indexes pre- and post-LT. Compared to controls, ALFF values increased in the Left Cerebelum 8, right orbital part of the inferior frontal gyrus (ORBinf), right superior occipital gyrus (SOG) and decreased in right PreCG and left middle frontal gyrus (MFG) in patients post-LT; dALFF values increased in the right temporal pole and middle temporal gyrus (TPOmid), right ORBinf, left caudate nucleus (CAU), right SOG and decreased in left PreCG, left PCUN, left ANG, left SMA and left MFG in patients post-LT. Compared to pre-LT, ALFF values of post-LT patients increased in the right calcarine fissure and surrounding cortex (CAL), right MOG and decreased in right cerebelum 8, left PCUN; dALFF values of post-LT patients decreased in right thalamus (THA), left posterior cingulate gyrus (PCG) and left MFG. The changes of ALFF in the left PCUN, right CAL and right MOG were correlated with change of digit symbol test (DST) scores ( P &amp;lt; 0.05). In summary, this study not only showcases the potential of ALFF/dALFF algorithms for assessing alterations in spontaneous neural activity in MHE, but also provides new insights into the altered brain functions in MHE patients 1 month after LT, which may facilitate the elucidation of elucidation of mechanisms underlying cognitive restoration post-LT in MHE patients.

Abstract The Fading Qualia Argument is perhaps the strongest argument supporting the view that in order for a system to be conscious, it does not need to be made of anything in particular, so long as its internal parts have the right causal relations to each other and to the system’s inputs and outputs. I show how the argument can be resisted given two key assumptions: that consciousness is associated with vagueness at its boundaries and that conscious neural activity has a particular kind of holistic structure.

Anaesthetics temporarily inhibit neural activity by acting on voltage-gated sodium channels and GABA receptors. Although their neurological mechanisms are well-defined, their wider cellular effects, especially in non-neuronal systems, are inadequately understood. This study utilized Solanum lycopersicum plant's root apex cells as a transparent model to examine anaesthetic-induced subcellular alterations via live-cell fluorescence imaging, immunostaining, and super-resolution microscopy. These findings demonstrate the hierarchical cascade of organelle dysfunction, such as mitochondria, lysosomes, vesicle trafficking, and nuclear architectures under anaesthesia in plants. The nucleus is identified as the main controller of recovery potential and cellular fate. In a time dependent experiment, it is found that plant cells exposed to lidocaine for up to 4 h can still recover mitochondrial potential, lysosomal function, and nuclear integrity when anaesthesia is removed. However, beyond 4 h the damage, especially to the nucleus, is irreversible, and cells proceeded to cell death. The data further demonstrate that organelles can recover after brief exposure, but prolonged exposure stops recovery, resulting in the irreversible degradation of the nucleus leading to complete cell death. The results may help to uncover organelle-related dysfunction under anaesthetic toxicity and provide a clearer understanding for minimizing or reversing such damage.

Objective This study aimed to compare the short-term clinical outcomes of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS), fluoxetine, and their combination in adolescents with first-onset obsessive-compulsive disorder (OCD) using a single-center retrospective, non-randomized design. Methods A single-center retrospective, non-randomized analysis was conducted on 167 adolescents (aged 12–18 years) diagnosed with obsessive-compulsive disorder (OCD) and treated at Dazhuang Hospital, Shandong Province, between January 2018 and June 2024. Based on treatment received, patients were categorized into three observational groups: LF-rTMS alone (n=32), fluoxetine alone (n=55), and combined fluoxetine plus LF-rTMS (n=80). LF-rTMS was delivered at 1 Hz over the right supplementary motor area (SMA), 20 sessions in total. Clinical outcomes were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Clinical Global Impressions-Improvement (CGI-I), Wisconsin Card Sorting Test (WCST), and quantitative electroencephalography (qEEG) at baseline, 2 weeks, and 4 weeks. Adverse events were monitored with the Treatment Emergent Symptom Scale (TESS). Results After 4 weeks of treatment, 68.26% of patients overall met the predefined response threshold (≥35% Y-BOCS reduction), with rates of 34.38% in the LF-rTMS group, 63.64% in the fluoxetine group, and 85.00% in the combined treatment group; remission (Y-BOCS ≤12) was observed in 12.5%, 20.0%, and 32.5% of the groups, respectively (χ²=27.85, P&amp;lt;0.001). Repeated-measures analyses further indicated significant Time and Time×Group effects for both Y-BOCS and CGI-I scores (P&amp;lt;0.001), confirming differential symptom trajectories across treatment groups. The combined group also showed comparatively greater improvements in cognitive performance (WCST indices) and more favorable qEEG changes relative to the monotherapy groups (P&amp;lt;0.001). No statistically significant differences in the distribution of adverse reactions were observed among the three groups (P = 0.549). Conclusion The findings suggest that combining fluoxetine with LF-rTMS may be associated with greater short-term improvements in symptom severity, cognitive function, and neural activity compared with monotherapy, while maintaining a favorable safety profile.

Introduction: Heart failure is associated with reduced cardiac output and cerebral hypoperfusion, which may disrupt neural activity in the brain. Resting electroencephalography (EEG) directly measures neural activity in the brain, however few studies have examined EEG measurements in individuals with heart failure. Research question: Our study aimed to address this gap in the literature by using high-density EEG to investigate resting neural activity in individuals with heart failure. Methods: Resting-state EEG was recorded from 13 individuals with heart failure (New York Heart Association class II-III) and 13 age- and sex- matched controls using a 128-electrode array. We calculated five EEG outcome measures in both sensor and source space: periodic alpha and beta power, dominant frequency, offset and slope. Mann-Whitney U tests were used to evaluate group differences in the global average of the main outcomes. Statistical comparisons EEG outcomes conducted using permutated-based Analysis of Covariance controlling for age and sex. Significance was set to p &lt; 0.05 (corrected for multiple comparisons using FDR). Results: We found significant reductions in beta power and dominant frequency in the heart failure group in both sensor and source space. The grand average beta power across 128 electrodes for the heart failure group was 0.36 Hz (±0.07) and 0.49 (±0.11) for the control group and this was significantly different between groups (U = 25.00, z = -3.05, p = 0.003). Source space analysis revealed significant reductions in beta power in 50 out of 64 cortical regions examined. For dominant frequency in sensor space, the grand average dominant frequency across 128 electrodes for the heart failure group was 8.60 Hz (± 0.77) and 9.75 Hz (± 1.35) for the control group and this was significantly different between groups (U = 36.00, z = -2.49, p = 0.012). Source space analysis found reduced dominant frequency in the heart failure group in 30 of the 64 cortical regions assessed. No significant between group differences were found for alpha power, offset and slope. Conclusions: Our findings revealed widespread reductions in beta power and dominant frequency, suggesting disrupted cortical activity which may underlie impairments in cognitive and sensorimotor function in individuals with heart failure.