scholarly journals Decreased Alpha Peak Frequency Is Linked to Episodic Memory Impairment in Pathological Aging

2021 ◽  
Vol 13 ◽  
Author(s):  
Delphine Puttaert ◽  
Vincent Wens ◽  
Patrick Fery ◽  
Antonin Rovai ◽  
Nicola Trotta ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Resting State ◽  
Early Stage ◽  
Brain Activity ◽  
Peak Frequency ◽  
Posterior Cingulate Cortex ◽  
Short Term ◽  
Slowing Down ◽  
Pathological Aging ◽  
Alpha Peak

The Free and Cued Selective Reminding Test (FCSRT) is a largely validated neuropsychological test for the identification of amnestic syndrome from the early stage of Alzheimer’s disease (AD). Previous electrophysiological data suggested a slowing down of the alpha rhythm in the AD-continuum as well as a key role of this rhythmic brain activity for episodic memory processes. This study therefore investigates the link between alpha brain activity and alterations in episodic memory as assessed by the FCSRT. For that purpose, 37 patients with altered FCSRT performance underwent a comprehensive neuropsychological assessment, supplemented by 18F-fluorodeoxyglucose positron emission tomography/structural magnetic resonance imaging (18FDG-PET/MR), and 10 min of resting-state magnetoencephalography (MEG). The individual alpha peak frequency (APF) in MEG resting-state data was positively correlated with patients’ encoding efficiency as well as with the efficacy of semantic cues in facilitating patients’ retrieval of previous stored word. The APF also correlated positively with patients’ hippocampal volume and their regional glucose consumption in the posterior cingulate cortex. Overall, this study demonstrates that alterations in the ability to learn and store new information for a relatively short-term period are related to a slowing down of alpha rhythmic activity, possibly due to altered interactions in the extended mnemonic system. As such, a decreased APF may be considered as an electrophysiological correlate of short-term episodic memory dysfunction accompanying pathological aging.

scholarly journals Alpha peak frequency is linked to episodic memory impairment in pathological aging

2021 ◽  
Author(s):  
Delphine Puttaert ◽  
Vincent Wens ◽  
Patrick Fery ◽  
Antonin Rovai ◽  
Nicola Trotta ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Resting State ◽  
Early Stage ◽  
Brain Activity ◽  
Peak Frequency ◽  
Short Term ◽  
Slowing Down ◽  
Pathological Aging ◽  
18Fdg Pet ◽  
Alpha Peak

The Free and Cued Selective Reminding Test (FCSRT) is a largely validated neuropsychological test for the identification of amnestic syndrome from the early stage of Alzheimers disease (AD). Previous electrophysiological data suggested a slowing down of the alpha rhythm in the AD-continuum as well as a key role of this rhythmic brain activity for episodic memory processes. This study therefore investigates the link between alpha brain activity and alterations in episodic memory as assessed by the FCSRT. For that purpose, 37 patients with altered FCSRT performance underwent a comprehensive neuropsychological assessment, supplemented by 18F-fluorodeoxyglucose positron emission tomography/structural magnetic resonance imaging (18FDG-PET/MR), and 10 minutes of resting-state magnetoencephalography (MEG). The individual alpha peak frequency (APF) in MEG resting-state data was positively correlated with patients encoding efficiency as well as with the efficacy of semantic cues in facilitating patients retrieval of previous stored word. The APF also correlated positively with patients hippocampal volume and their regional glucose consumption in the posterior cingulate cortex. Overall, this study demonstrates that alterations in the ability to learn and store new information for a relatively short-term period are related to a slowing down of alpha rhythmic activity, possibly due to altered interactions in the extended mnemonic system. As such, a decreased APF may be considered as an electrophysiological correlate of short-term episodic memory dysfunction accompanying pathological aging.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

2018 ◽  
Vol 71 ◽  
pp. 149-155 ◽  
Author(s):  
Brian Scally ◽  
Melanie Rose Burke ◽  
David Bunce ◽  
Jean-Francois Delvenne
Keyword(s):  
Resting State ◽  
Healthy Aging ◽  
Peak Frequency ◽  
Eeg Power ◽  
Alpha Peak

scholarly journals Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

2018 ◽  
Author(s):  
Antonio Ulloa ◽  
Barry Horwitz
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Short Term Memory ◽  
Brain Activity ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Task Execution ◽  
Short Term ◽  
Visual Short Term Memory ◽  
And Task

AbstractEstablishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.Author SummaryStudies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.

scholarly journals The frequency gradient of human resting-state brain oscillations follows cortical hierarchies

2019 ◽  
Author(s):  
Keyvan Mahjoory ◽  
Jan-Mathijs Schoffelen ◽  
Anne Keitel ◽  
Joachim Gross
Keyword(s):  
Cortical Thickness ◽  
Resting State ◽  
Brain Activity ◽  
Brain Area ◽  
Peak Frequency ◽  
Higher Order ◽  
Hierarchical Level ◽  
Spatial Gradient ◽  
Brain Oscillations ◽  
Posterior Anterior

AbstractThe human cortex is characterized by local morphological features such as cortical thickness, myelin content and gene expression that change along the posterior-anterior axis. We investigated if these structural gradients are associated with a similar gradient in a prominent feature of brain activity – namely the frequency of brain oscillations. In resting-state MEG recordings from healthy participants (N=187), we found that the strongest peak frequency in a brain area decreases significantly, gradually and robustly along the posterior-anterior axis following the global hierarchy from early sensory to higher-order areas. This spatial gradient of peak frequency was significantly anticorrelated with the cortical thickness of corresponding areas representing a proxy of the cortical hierarchical level. This result indicates that the intrinsic ‘resonance’ frequency decreases systematically from early sensory to higher-order areas and establishes a new structure-function relationship pertaining to brain oscillations as a core organizational principle that may underlie hierarchical specialization in the brain.

Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation

2004 ◽  
Vol 16 (9) ◽  
pp. 1484-1492 ◽  
Author(s):  
Michael D. Greicius ◽  
Vinod Menon
Keyword(s):  
Episodic Memory ◽  
Cognitive Processing ◽  
Neural Activity ◽  
Default Mode Network ◽  
Brain Activity ◽  
Block Design ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
High Demand ◽  
Default Mode

Deactivation refers to increased neural activity during low-demand tasks or rest compared with high-demand tasks. Several groups have reported that a particular set of brain regions, including the posterior cingulate cortex and the medial prefrontal cortex, among others, is consistently deactivated. Taken together, these typically deactivated brain regions appear to constitute a default-mode network of brain activity that predominates in the absence of a demanding external task. Examining a passive, block-design sensory task with a standard deactivation analysis (rest epochs vs. stimulus epochs), we demonstrate that the default-mode network is undetectable in one run and only partially detectable in a second run. Using independent component analysis, however, we were able to detect the full default-mode network in both runs and to demonstrate that, in the majority of subjects, it persisted across both rest and stimulus epochs, uncoupled from the task waveform, and so mostly undetectable as deactivation. We also replicate an earlier finding that the default-mode network includes the hippocampus suggesting that episodic memory is incorporated in default-mode cognitive processing. Furthermore, we show that the more a subject's default-mode activity was correlated with the rest epochs (and “deactivated” during stimulus epochs), the greater that subject's activation to the visual and auditory stimuli. We conclude that activity in the default-mode network may persist through both experimental and rest epochs if the experiment is not sufficiently challenging. Time-series analysis of default-mode activity provides a measure of the degree to which a task engages a subject and whether it is sufficient to interrupt the processes—presumably cognitive, internally generated, and involving episodic memory—mediated by the default-mode network.

scholarly journals Effects of Physical Exercise on Individual Resting State EEG Alpha Peak Frequency

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Boris Gutmann ◽  
Andreas Mierau ◽  
Thorben Hülsdünker ◽  
Carolin Hildebrand ◽  
Axel Przyklenk ◽  
...  
Keyword(s):  
Exercise Training ◽  
Physical Exercise ◽  
Cognitive Processing ◽  
Resting State ◽  
Peak Frequency ◽  
External Input ◽  
Acute Bout ◽  
Positive Effects ◽  
Eeg Alpha ◽  
Alpha Peak

Previous research has shown that both acute and chronic physical exercises can induce positive effects on brain function and this is associated with improvements in cognitive performance. However, the neurophysiological mechanisms underlying the beneficial effects of exercise on cognitive processing are not well understood. This study examined the effects of an acute bout of physical exercise as well as four weeks of exercise training on the individual resting state electroencephalographic (EEG) alpha peak frequency (iAPF), a neurophysiological marker of the individual’s state of arousal and attention, in healthy young adults. The subjects completed a steady state exercise (SSE) protocol or an exhaustive exercise (EE) protocol, respectively, on two separate days. EEG activity was recorded for 2 min before exercise, immediately after exercise, and after 10 min of rest. All assessments were repeated following four weeks of exercise training to investigate whether an improvement in physical fitness modulates the resting state iAPF and/or the iAPF response to an acute bout of SSE and EE. The iAPF was significantly increased following EE (P=0.012) but not following SSE. It is concluded that the iAPF is increased following intense exercise, indicating a higher level of arousal and preparedness for external input.

scholarly journals Individual alpha peak frequency is slower in schizophrenia and related to deficits in visual perception and cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ian S. Ramsay ◽  
Peter Lynn ◽  
Brandon Schermitzler ◽  
Scott Sponheim
Keyword(s):  
Visual Perception ◽  
Visual Attention ◽  
Resting State ◽  
Peak Frequency ◽  
Attention Task ◽  
Eyes Closed ◽  
Global Cognition ◽  
The Relationship ◽  
The Brain ◽  
Alpha Peak

AbstractThe brain at rest generates cycles of electrical activity that have been shown to be abnormal in people with schizophrenia. The alpha rhythm (~ 10 Hz) is the dominant resting state electrical cycle and each person has a propensity toward a particular frequency of oscillation for this rhythm. This individual alpha peak frequency (IAPF) is hypothesized to be central to visual perceptual processes and may have downstream influences on cognitive functions such as attention, working memory, or problem solving. In the current study we sought to determine whether IAPF was slower in schizophrenia, and whether lower IAPF predicted deficits in visual perception and cognition that are often observed in schizophrenia. Eyes-closed resting state EEG activity, visual attention, and global cognitive functioning were assessed in individuals with schizophrenia (N = 104) and a group of healthy controls (N = 101). Compared to controls, the schizophrenia group showed slower IAPF and was associated with poorer discrimination of visual targets and nontargets on a computerized attention task, as well as impaired global cognition measured using neuropsychological tests across groups. Notably, disruptions in visual attention fully mediated the relationship between IAPF and global cognition across groups. The current findings demonstrate that slower alpha oscillatory cycling accounts for global cognitive deficits in schizophrenia by way of impairments in perceptual discrimination measured during a visual attention task.

Biomarker-based treatment stratification in ADHD: An out-of-sample validation

2021 ◽  
Author(s):  
Helena Voetterl ◽  
Guido van Wingen ◽  
Giorgia Michelini ◽  
Kristi Griffiths ◽  
Evian Gordon ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Resting State ◽  
Peak Frequency ◽  
Hyperactivity Disorder ◽  
Remission Rates ◽  
Out Of Sample ◽  
The Right ◽  
Alpha Peak ◽  
Age And Sex

Abstract Attention-deficit/hyperactivity disorder (ADHD) is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.The present study introduces a clinically interpretable and actionable, age- and sex-standardized biomarker based on individual alpha peak frequency (iAPF) assessed during resting-state electroencephalography (EEG). The biomarker was developed in a heterogeneous sample (N=4249), and stratifies patients with a higher iAPF to Methylphenidate (MPH; N=336) and those with a lower iAPF to Neurofeedback (NFB; N=136), resulting in a predicted gain in normalized remission of 17-30%. Blinded out-of-sample validation studies for MPH (N=58) and NFB (N=96) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.These findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

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