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Stroke patient demographic and lesion characteristics. See Kielar et al. (2016) and Shah-Basak et al. (2020) for further information.
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Decades of electrophysiological work have demonstrated the presence of “spectral slowing” in stroke patients – a prominent shift in the power spectrum towards lower frequencies, most evident in the vicinity of the lesion itself. Despite the reliability of this slowing as a marker of dysfunctional tissue across patient groups as well as animal model...
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... sample included twenty-three chronic stroke patients (seventeen men, six women) drawn from two prior studies ( Kielar et al., 2016; Shah- Basak et al., 2020), with an average age of 63.4 years (SD = 13.0 years), and 16.9 years (SD = 3.24 years) of education. All patients had a single left-hemispheric stroke (mostly ischemic, see Table 1) at least six months prior to data collection (average time post onset = 4.6 years, SD = 3.5 years), and demonstrated symptoms of aphasia. All patients were righthanded except one. ...Context 2
... patients were righthanded except one. See Table 1 for further details. Patients were matched for age (t(44) = 1.028, p = 0.310) and education (t(44) = 0.928, p = 0.358) with a sample of twenty-three healthy control participants (seventeen men, six women) from the same studies as above. ...Citations
... Our results show that systematic non-stationarities in alpha band activity are driven by oscillatory changes and not due to alterations in the aperiodic signal. This is important given that previous time-on-task studies (Benwell et al., 2019;Boksem et al., 2005;Cajochen et al., 1995;Sadaghiani et al., 2010) did not account for the aperiodic exponent, which has been shown to confound spectral results regarding EEG changes with aging (Merkin et al., 2021;Tröndle et al., 2022), cognitive performance (Ouyang et al., 2020), and disease states (Johnston et al., 2023;Pani et al., 2022;Robertson et al., 2019). We found no significant changes in the aperiodic exponent across blocks. ...
Fluctuations in oscillatory brain activity have been shown to co-occur with variations in task performance. More recently, part of these fluctuations has been attributed to long-term (>1hr) monotonous trends in the power and frequency of alpha oscillations (8-13 Hz). Here we tested whether these time-on-task changes in EEG activity are limited to activity in the alpha band and whether they are linked to task performance. Thirty-six participants performed 900 trials of a two-alternative forced choice visual discrimination task with confidence ratings. Pre- and post-stimulus spectral power (1-40Hz) and aperiodic (i.e., non-oscillatory) components were compared across blocks of the experimental session and tested for relationships with behavioural performance. We found that time-on-task effects on oscillatory EEG activity were primarily localised within the alpha band, with alpha power increasing and peak alpha frequency decreasing over time, even when controlling for aperiodic contributions. Aperiodic, broadband activity on the other hand did not show time-on-task effects in our data set. Importantly, time-on-task effects in alpha frequency and power explained variability in single-trial reaction times. Moreover, controlling for time-on-task effectively removed the relationships between alpha activity and reaction times. However, time-on-task effects did not affect other EEG signatures of behavioural performance, including post-stimulus predictors of single-trial decision confidence. Therefore, our results dissociate alpha-band brain-behaviour relationships that can be explained away by time-on-task from those that remain after accounting for it - thereby further specifying the potential functional roles of alpha in human visual perception.
... These results thus lend support to an interpretation linking AD to abnormal neural oscillations relative to cognitively healthy controls. Intriguingly, this contrasts with a growing body of literature showing changes in the spectral aperiodic exponent are associated with a range of neuropsychological pathologies (Pani et al., 2022), including ADHD (Karalunas et al., 2022;Robertson et al., 2019), schizophrenia (Molina et al., 2020;Peterson et al., 2021), stroke (Johnston et al., 2023), and Parkinson's disease (Belova et al., 2021). Additionally, the present results suggest that oscillatory abnormalities captured in the SPR are primarily driven by high frequency (8-30 Hz) power decreases. ...
... Hence, we establish that AD differentiates from the often-found aging effect on aperiodic parameters, with a periodic-specific effect found to differentiate between clinical and non-clinical participants. Interestingly, conceptually similar work examining spectral slowing in the context of stroke found influences of both periodic and aperiodic parameters (Johnston et al., 2023). These results highlight that differences in power ratios reflecting 'spectral slowing' can be driven by different underlying changes, and that spectral parameterizing can adjudicate between different changes. ...
... 33,34 Recently, converging evidence points at the conclusion that changes in the aperiodic component are associated with cognitive 28,35 and perceptual states, 36 development, 37 aging, 38 and pathological conditions. [39][40][41][42][43] Thus, ignoring the aperiodic component can lead to a misrepresentation and misinterpretation of physiological mechanisms. 28,37 Furthermore, the use of a priori frequency bands for oscillatory analyses can result in misinterpreting aperiodic activity as periodic activity, and thus bias the power of actual physiological oscillations, or even lead to extracting power from oscillations that simply do not exist in some cases. ...
Cognitive symptoms in Parkinson’s disease (PD) are common and can significantly affect patients’ quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioral and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified electroencephalogram (EEG) aperiodic activity as meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in PD during cognitive control and characterize its possible association with behavior.
Here, we recorded high-density EEG (HD-EEG) in 30 healthy controls and 30 PD patients during a Simon task. We analyzed task-related behavioral data in the context of the activation-suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels.
Our results showed behavioral alterations of cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in PD. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting state, pre- and post-stimulus phases all across the scalp and cortex confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behavior or clinical features.
Our findings provide evidence that EEG aperiodic activity in PD is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behavior-related differences observed in PD are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behavior.
Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer’s disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.
