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Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01–0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Major depressive disorder (MDD) has been associated with aberrant effective connectivity (EC) among the default mode network (DMN), salience network (SN), and central executive network (CEN)--collectively referred to as triple networks. However, prior research has predominantly concentrated on broad frequency bands (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the influence of distinct rhythms on triple network causal dynamics. In the present study, we aim to investigate EC alterations within the triple networks across various frequency bands in patients with MDD. Utilizing a data-driven frequency decomposition approach and a multivariate Granger causality analysis, we characterized frequency-specific EC patterns of triple networks in 49 MDD patients and 54 healthy controls. A support vector machine classifier was subsequently employed to assess the discriminative capacity of the frequency-specific EC features. Our findings revealed that, compared to controls, patients exhibited not only enhanced mean EC within the CEN in the conventional frequency band (0.01-0.08 Hz), but also decreased mean EC from the SN to the DMN in a higher frequency band (0.12-0.18 Hz), and increased mean EC from the CEN to the SN in a sub-frequency band (0.04-0.08 Hz); the latter was significantly correlated with disease severity. Moreover, optimal classification performance for distinguishing patients from controls was attained by combining EC features across all three frequency bands, with the area under the curve (AUC) value of 0.8831 and the corresponding accuracy, sensitivity, and specificity of 89.97%, 92.63%, and 87.32%, respectively. These insights into EC changes within the triple networks across multiple frequency bands offer valuable perspectives on the neurobiological basis of MDD and could aid in developing frequency-specific EC features as potential biomarkers for disease diagnosis. [ABSTRACT FROM AUTHOR]

Objective:Previous behavioral studies have reported the potential of transcranial alternating current stimulation in analyzing the causal relationship between neural activity and behavior. However, the efficacy of tACS on motor performance and learning in healthy individuals remains unclear. This systematic reviewexamines the effectiveness of tACS on motor performance and motor learning in healthy individuals. Methods: Literature was systematically searched through the Cochrane Library, PubMed, EMBASE, and Web of Science until 16 October 2022. Studies were eligible for review if they were randomized, parallel, or crossover experimental designs and reported the efficacy of tACS on motor performance and motor learning in healthy adults. Review Manager 5.3 was used to evaluate the methodological quality and analyze the combined effect. Results: Ten studies (270 participants) met all the inclusion criteria. The results showed that motor performance was not significantly greater than that with sham tACS stimulation [I2 = 44%, 95% CI (--0.01, 0.35), p = 0.06, standardized mean difference = 0.17], whereas motor learning ability improved significantly [I2 = 33%, 95% CI (1.03, 0.31), p = 0.0002, SMD = -0.67]. Subgroup analysis found that gamma bend tACS could affect the changes in motor performance (I2 = 6%, 95% CI (0.05, 0.51), p = 0.02, SMD = 0.28), and online tACS did as well [I2 = 54%, 95% CI (0.12, 0.56), p = 0.002, SMD = 0.34]. Conclusion: The results showed that tACS effectively improves motor performance (gamma band and online mode) and motor learning in healthy individuals, which indicates that tACS may be a potential therapeutic tool to improve motor behavioral outcomes. However, further evidence is needed to support these promising results. [ABSTRACT FROM AUTHOR]

The brain is a highly complex and delicate system, and its internal neural processes are manifested as the interweaving and superposition of multi-frequency neural signals. However, traditional brain network studies are often limited to the whole frequency band or a specific frequency band, ignoring the potentially profound impact of the diversity of information within the frequency on the dynamics of brain networks. To comprehensively and deeply analyze this phenomenon, the present study is devoted to exploring the specific performance of brain networks at different frequencies. We used the maximum overlap discrete wavelet transform technique to finely divide the time series data into the following frequency bands: scale 1 (0.125-0.25 Hz), scale 2 (0.06-0.125 Hz), scale 3 (0.03-0.06 Hz) and scale 4 (0.015-0.03 Hz). Based on these frequency bands, we constructed multilayer networks from both dynamic and static perspectives, respectively. From the dynamic perspective, we quantitatively evaluated the dynamic differences among different frequency bands using metrics such as flexibility, promiscuity, integration, and recruitment, and found that scale 3 and scale 4 bands performed particularly well. In contrast, from a static perspective, we measured the cross-frequency interaction capability between different frequency bands through metrics such as multilayer clustering coefficient and entropy of multiplexing degree, and the results show that scale 2, scale 3, and scale 4 band networks have enhanced global integration capability and local capability. In addition, we explored the correlation of gender and age with the properties of brain networks in different frequency bands. In the scale 1 frequency band, the organization of brain functions showed significant gender differences, while in the scale 2 frequency band, there was a significant correlation between age and global efficiency. By integrating the dual perspectives of time and frequency domains, this study not only reveals the critical role of frequency specificity in the brain's information processing and functional organization but also provides new perspectives for understanding the complex working mechanisms of the brain as well as gender- and age-related cognitive differences., 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 © 2024 Elsevier B.V. All rights reserved.)

Background: Apathy is among the most prevalent and incapacitating nonmotor symptoms of Parkinson's disease (PD). PD patients with apathy (PD-A) have been reported to have abnormal spontaneous brain activity mainly in 0.01-0.08 Hz. However, the frequency-dependence of brain activity in PD-A remains unclear. Therefore, this study aimed to examine whether abnormalities in PD-A are associated with specific frequency bands. Materials and methods: Overall, 28 patients with PD-A, 19 PD patients without apathy (PD-NA), and 32 gender-, age-matched healthy controls (HCs) were enrolled. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data, demographic information, and neuropsychological assessments, including apathy, depression, anxiety and cognitive function for every participant. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) were calculated in the conventional (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) frequency bands based on statistical parametric mapping (SPM12) and RESTplus V1.25. Two-sample t-tests were performed to compare the differences among the three groups. Results: PD-A reduced ALFF in the right anterior cingulate gyri in the slow-5 band and decreased fALFF in the right middle frontal gyrus in the conventional band, compared to patients with PD-NA. However, PerAF, ReHo, and DC could not distinguish PD-A from PD-NA in the three bands. PD-A had higher ALFF and fALFF in the left middle occipital gyrus and lower fALFF in the bilateral insula in the slow-5 band compared to the HCs. Furthermore, abnormal DC value in hippocampus and parahippocampus was observed separately in the conventional band and in the slow-4 band between PD-A and HCs. Moreover, PD-A and PD-NA showed lower ReHo in cerebellum in the three bands compared to the HCs. Conclusion: Our study revealed that PD-A and PD-NA might have different neurophysiological mechanisms. Concurrently, the ALFF in the slow-5 band and fALFF in the conventional band were sensitive in differentiating PD-A from PD-NA. The influence of apathy on the disease can be considered in the future research on PD, with the effects of frequency band taken into account when analyzing spontaneous brain activities in PD-A. [ABSTRACT FROM AUTHOR]

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01–0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain. • We proposed an RSF+SVM framework which achieved satisfactory RW-SD classification performance. • We revealed the frequency-specific topology changes caused by SD using rs-fMRI data. • Hubs of spectral networks are more likely to be discriminative nodes. • The cerebellum was a hub region after SD from the frequency perspective instead of in the time domain. [ABSTRACT FROM AUTHOR]

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.

BackgroundDepression induces an early onset of Parkinson’s disease (PD), aggravates dyskinesia and cognitive impairment, and accelerates disease progression. However, it is very difficult to identify and diagnose PD with depression (PDD) in the early clinical stage. Few studies have suggested that the changes in neural networks are associated with PDD, while degree centrality (DC) has been documented to be effective in detecting brain network changes.ObjectivesThe objectives of this study are to explore DC changes between patients with PDD and without depression (PDND) and to find the key brain hubs involved with depression in PD patients.MethodsOne hundred and four PD patients and 54 healthy controls (HCs) underwent brain resting-state functional magnetic resonance imaging. The Data Processing and Analysis of Brain Imaging and Resting-State Functional Magnetic Resonance Data Analysis Toolkit were used for processing and statistical analysis. The DC value of each frequency band was calculated. One-way analysis of variance and a two-sample t-test for post hoc comparison were used to compare the differences of the DC values in different frequency bands among PDD, PDND, and healthy control group. Gaussian random field was used for multiple comparison correction. Pearson correlation analysis was performed between each individual’s DC map and clinical indicators.ResultsThe DC value of different brain regions changed in PDD and PDND in different frequency bands. The prefrontal lobe, limbic system, and basal ganglia were the main brain regions involved. PDD patients showed a wider range and more abnormal brain areas in the slow-4 frequency band (0.027–0.073 Hz) compared to the HCs. PDD showed a decreased DC value in the medial frontal gyrus, bilateral cuneus gyrus, right lingual gyrus, bilateral supplementary motor area (SMA), bilateral superior frontal gyrus, and left paracentral lobule, but an increased DC value in the bilateral brainstem, midbrain, bilateral parahippocampal gyrus, cerebellum, left superior temporal gyrus, bilateral insula, left fusiform gyrus, and left caudate nucleus in the traditional frequency band (0.01–0.08 Hz) compared to PDND patients. PDND patients displayed more abnormal functions in the basal ganglia in the slow-4 frequency band.ConclusionThe DC changes in PDD and PDND are frequency dependent and frequency specific. The medial frontal gyrus, SMA, and limbic system may be the key hubs for depression in PD.

Background: Depression induces an early onset of Parkinson's disease (PD), aggravates dyskinesia and cognitive impairment, and accelerates disease progression. However, it is very difficult to identify and diagnose PD with depression (PDD) in the early clinical stage. Few studies have suggested that the changes in neural networks are associated with PDD, while degree centrality (DC) has been documented to be effective in detecting brain network changes. Objectives: The objectives of this study are to explore DC changes between patients with PDD and without depression (PDND) and to find the key brain hubs involved with depression in PD patients. Methods: One hundred and four PD patients and 54 healthy controls (HCs) underwent brain resting-state functional magnetic resonance imaging. The Data Processing and Analysis of Brain Imaging and Resting-State Functional Magnetic Resonance Data Analysis Toolkit were used for processing and statistical analysis. The DC value of each frequency band was calculated. One-way analysis of variance and a two-sample t -test for post hoc comparison were used to compare the differences of the DC values in different frequency bands among PDD, PDND, and healthy control group. Gaussian random field was used for multiple comparison correction. Pearson correlation analysis was performed between each individual's DC map and clinical indicators. Results: The DC value of different brain regions changed in PDD and PDND in different frequency bands. The prefrontal lobe, limbic system, and basal ganglia were the main brain regions involved. PDD patients showed a wider range and more abnormal brain areas in the slow-4 frequency band (0.027–0.073 Hz) compared to the HCs. PDD showed a decreased DC value in the medial frontal gyrus, bilateral cuneus gyrus, right lingual gyrus, bilateral supplementary motor area (SMA), bilateral superior frontal gyrus, and left paracentral lobule, but an increased DC value in the bilateral brainstem, midbrain, bilateral parahippocampal gyrus, cerebellum, left superior temporal gyrus, bilateral insula, left fusiform gyrus, and left caudate nucleus in the traditional frequency band (0.01–0.08 Hz) compared to PDND patients. PDND patients displayed more abnormal functions in the basal ganglia in the slow-4 frequency band. Conclusion: The DC changes in PDD and PDND are frequency dependent and frequency specific. The medial frontal gyrus, SMA, and limbic system may be the key hubs for depression in PD. [ABSTRACT FROM AUTHOR]

• Machine learning approach for PD investigation. • Multi-frequency based machine learning in PD. • Frequency specificities of ALFF maps in PD. The application of resting state functional MRI (RS-fMRI) in Parkinson's disease (PD) was widely performed using standard statistical tests, however, the machine learning (ML) approach has not yet been investigated in PD using RS-fMRI. In current study, we utilized the mean regional amplitude values as the features in patients with PD (n = 72) and in healthy controls (HC, n = 89). The t -test and linear support vector machine were employed to select the features and make prediction, respectively. Three frequency bins (Slow-5: 0.0107–0.0286 Hz; Slow-4: 0.0286–0.0821 Hz; conventional: 0.01–0.08 Hz) were analyzed. Our results showed that the Slow-4 may provide important information than Slow-5 in PD, and it had almost identical classification performance compared with the Combined (Slow-5 and Slow-4) and conventional frequency bands. Similar with previous neuroimaging studies in PD, the discriminative regions were mainly included the disrupted motor system, aberrant visual cortex, dysfunction of paralimbic/limbic and basal ganglia networks. The lateral parietal lobe, such as right inferior parietal lobe (IPL) and supramarginal gyrus (SMG), was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the ML approach is a promising choice for detecting abnormal regions in PD, and a multi-frequency scheme would provide us more specific information. [ABSTRACT FROM AUTHOR]

Envelope following responses (EFRs) evoked by the periodicity of voicing in vowels are elicited at the fundamental frequency of voice (f 0), irrespective of the harmonics that initiate it. One approach of improving the frequency specificity of vowel stimuli without increasing test-time is by altering the f 0 selectively in one or more formants. The harmonics contributing to an EFR can then be differentiated by the unique f 0 at which the EFRs are elicited. The advantages of using such an approach would be increased frequency specificity and efficiency, given that multiple EFRs can be evaluated in a certain test-time. However, multiple EFRs elicited simultaneously could interact and lead to altered amplitudes and outcomes. To this end, the present study aimed to evaluate: (i) if simultaneous recording of two EFRs, one elicited by harmonics in the first formant (F1) and one elicited by harmonics in the second and higher formants (F2+), leads to attenuation or enhancement of EFR amplitude, and (ii) if simultaneous measurement of two EFRs affects its accuracy and anticipated efficiency. In a group of 22 young adults with normal hearing, EFRs were elicited by F1 and F2+ bands of /u/, /a/ and /i/ when F1 and F2+ were presented independently (individual), when F1 and F2+ were presented simultaneously (dual), and when F1 or F2+ was presented with spectrally matched Gaussian noise of the other (noise). Repeated-measures analysis of variance indicated no significant group differences in EFR amplitudes between any of the conditions, suggesting minimal between-EFR interactions. Between-participant variability was evident, however, significant changes were evident only in a third of the participants for the stimulus /u/ F1. For the majority of stimuli, the change between individual and dual conditions was positively correlated with the change between individual and noise conditions, suggesting that interaction-based changes in EFR amplitude, when present, were likely due to the restriction of cochlear regions of excitation in the presence of a competing stimulus. The amplitude of residual noise was significantly higher in the dual or noise relative to the individual conditions, although the mean differences were very small (<3 nV). F-test-based detection of EFRs, commonly used to determine the presence of an EFR, did not vary across conditions. Further, neither the mean reduction in EFR amplitude nor the mean increase in noise amplitude in dual relative to individual conditions was large enough to alter the anticipated gain in efficiency of simultaneous EFR recordings. Together, results suggest that the approach of simultaneously recording two vowel-evoked EFRs from different formants for improved frequency-specificity does not alter test accuracy and is more time-efficient than evaluating EFRs to each formant individually. • Modified vowels can elicit formant-specific envelope following responses (EFR). • Formant-specific EFRs improve frequency specificity of broadband vowels. • Simultaneously recorded formant-specific EFRs are cochlear-place-specific. • Simultaneously recorded formant-specific EFRs increase test efficiency. [ABSTRACT FROM AUTHOR]

• Explore striatal FC in ASD from dynamic and static perspectives in the slow-4 and slow-5 bands. • Aberrant striatal dFC and sFC have great performance in ASD classification. • Examining brain activity using dynamic and static FC provides a comprehensive view. Dysfunctions of the striatum have been repeatedly observed in autism spectrum disorder (ASD). However, previous studies have explored the static functional connectivity (sFC) of the striatum in a single frequency band, ignoring the dynamics and frequency specificity of brain FC. Therefore, we investigated the dynamic FC (dFC) and sFC of the striatum in the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) frequency bands. Data of 47 ASD patients and 47 typically developing (TD) controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. A seed-based approach was used to compute the dFC and sFC. Then, a two-sample t-test was performed. For regions showing abnormal sFC and dFC, we performed clinical correlation analysis and constructed support vector machine (SVM) models. The middle frontal gyrus (MFG), precuneus, and medial superior frontal gyrus (mPFC) showed both dynamic and static alterations. The reduced striatal dFC in the right MFG was associated with autism symptoms. The dynamic‒static FC model had a great performance in ASD classification, with 95.83 % accuracy. The striatal dFC and sFC were altered in ASD, which were frequency specific. Examining brain activity using dynamic and static FC provides a comprehensive view of brain activity. [Display omitted] [ABSTRACT FROM AUTHOR]

We examined the frequency specificity of amplitude envelope patterns in 4 frequency bands, which universally appeared through factor analyses applied to power fluctuations of critical-band filtered speech sounds in 8 different languages/dialects [Ueda and Nakajima (2017). Sci. Rep., 7 (42468)]. A series of 3 perceptual experiments with noise-vocoded speech of Japanese sentences was conducted. Nearly perfect (92–94%) mora recognition was achieved, without any extensive training, in a control condition in which 4-band noise-vocoded speech was employed (Experiments 1–3). Blending amplitude envelope patterns of the frequency bands, which resulted in reducing the number of amplitude envelope patterns while keeping the average spectral levels unchanged, revealed a clear deteriorating effect on intelligibility (Experiment 1). Exchanging amplitude envelope patterns brought generally detrimental effects on intelligibility, especially when involving the 2 lowest bands ( ≲ 1850 Hz; Experiment 2). Exchanging spectral levels averaged in time had a small but significant deteriorating effect on intelligibility in a few conditions (Experiment 3). Frequency specificity in low-frequency-band envelope patterns thus turned out to be conspicuous in speech perception. [ABSTRACT FROM AUTHOR]

Abstract: Traditional task‐evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low‐frequency steady‐state brain response (lfSSBR) reflects frequency‐tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra‐slow frequency band (<0.1 Hz), suggesting a multifrequency energy reallocation. The consistency of power and variability was demonstrated by the high correlation (r > .955) of their spatial distribution and brain–behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency‐specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks. [ABSTRACT FROM AUTHOR]

Numerous brain oscillations are well organized into several brain rhythms to support complex brain activities within distinct frequency bands. These rhythms temporally coexist in the same or different brain areas and may interact with each other with specific properties and physiological functions. However, the identification and evaluation of these various brain rhythms derived from BOLD-fMRI signals are obscure. To address this issue, we introduced a data-driven method named Complementary Ensemble Empirical Mode Decomposition (CEEMD) to automatically decompose the BOLD oscillations into several brain rhythms within distinct frequency bands. Thereafter, in order to evaluate the performance of CEEMD in the detection of subtle BOLD signals, a novel CEEMD-based high-dimensional pattern classification framework was proposed to accurately identify mild cognitive impairment individuals from the healthy controls. Our results showed CEEMD is a stable frequency decomposition method. Furthermore, CEEMD-based frequency specific topological profiles provided a classification accuracy of 93.33%, which was saliently higher than that of the conventional frequency separation based scheme. Importantly, our findings demonstrated that CEEMD could provide an effective means for brain oscillation separation, by which a more meaningful frequency bins could be used to detect the subtle changes embedded in the BOLD signals. [ABSTRACT FROM AUTHOR]

Objective: In order to help a surgeon make the best decision, a more objective method of measuring ossicular motion is required. Methods: A laser Doppler vibrometer was mounted on a surgical microscope. To measure ossicular chain vibrations, eight patients with cochlear implants were investigated. To assess the motions of the ossicular chain, velocities at five points were measured with tonal stimuli of 1 and 3 kHz, which yielded reproducible results. The sequential amplitude change at each point was calculated with phase shifting from the tonal stimulus. Motion of the ossicular chain was visualized from the averaged results using the graphics application. Results: The head of the malleus and the body of the incus showed synchronized movement as one unit. In contrast, the stapes (incudostapedial joint and posterior crus) moved synchronously in opposite phase to the malleus and incus. The amplitudes at 1 kHz were almost twice those at 3 kHz. Conclusions: Our results show that the malleus and incus unit and the stapes move with a phase difference. [ABSTRACT FROM AUTHOR]

The cochlea behaves like a bank of band-pass filters, segregating information into different frequency channels. Some aspects of perception reflect processing within individual channels, but others involve the integration of information across them. One instance of this is sound localization, which improves with increasing bandwidth. The processing of binaural cues for sound location has been studied extensively. However, although the advantage conferred by bandwidth is clear, we currently know little about how this additional information is combined to form our percept of space. We investigated the ability of cells in the auditory system of guinea pigs to compare interaural level differences (ILDs), a key localization cue, between tones of disparate frequencies in each ear. Cells in auditory cortex believed to be integral to ILD processing (excitatory from one ear, inhibitory from the other: EI cells) compare ILDs separately over restricted frequency ranges which are not consistent with their monaural tuning. In contrast, cells that are excitatory from both ears (EE cells) show no evidence of frequency-specific processing. Both cell types are explained by a model in which ILDs are computed within separate frequency channels and subsequently combined in a single cortical cell. Interestingly, ILD processing in all inferior colliculus cell types (EE and EI) is largely consistent with processing within single, matched-frequency channels from each ear. Our data suggest a clear constraint on the way that localization cues are integrated: cortical ILD tuning to broadband sounds is a composite of separate, frequency specific, binaurally sensitive channels. This frequency-specific processing appears after the level of the midbrain. [ABSTRACT FROM AUTHOR]

The topological organization underlying the human brain was extensively investigated using resting-state functional magnetic resonance imaging, focusing on a low frequency of signal oscillation from 0.01 to 0.1 Hz. However, the frequency specificities with regard to the topological properties of the brain networks have not been fully revealed. In this study, a novel complementary ensemble empirical mode decomposition (CEEMD) method was used to separate the fMRI time series into five characteristic oscillations with distinct frequencies. Then, the small world properties of brain networks were analyzed for each of these five oscillations in patients (n = 67) with depressed Parkinson's disease (DPD, n = 20) , non-depressed Parkinson's disease (NDPD, n = 47) and healthy controls (HC, n = 46). Compared with HC, the results showed decreased network efficiency in characteristic oscillations from 0.05 to 0.12 Hz and from 0.02 to 0.05 Hz for the DPD and NDPD patients, respectively. Furthermore, compared with HC, the most significant inter-group difference across five brain oscillations was found in the basal ganglia (0.01 to 0.05 Hz) and paralimbic-limbic network (0.02 to 0.22 Hz) for the DPD patients, and in the visual cortex (0.02 to 0.05 Hz) for the NDPD patients. Compared with NDPD, the DPD patients showed reduced efficiency of nodes in the basal ganglia network (0.01 to 0.05 Hz). Our results demonstrated that DPD is characterized by a disrupted topological organization in large-scale brain functional networks. Moreover, the CEEMD analysis suggested a prominent dissociation in the topological organization of brain networks between DPD and NDPD in both space and frequency domains. Our findings indicated that these characteristic oscillatory activities in different functional circuits may contribute to distinct motor and non-motor components of clinical impairments in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Optogenetic approaches have allowed researchers to address complex questions about behavior that were previously unanswerable. However, as optogenetic procedures involve a large parameter space across multiple dimensions, it is crucial to consider such parameters in conjunction with the behaviors under study. Here, we discuss strategies to optimize optogenetic approaches with complex behavior by identifying critical experimental design considerations, including frequency specificity, temporal precision, activity-controlled optogenetics, stimulation pattern, and cell-type specificity. We highlight potential limitations or theoretical considerations to be made when manipulating each of these factors of optogenetic experiments. This overview emphasizes the importance of optimizing optogenetic study design to enhance the conclusions that can be drawn about the neuroscience of behavior. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

Major depressive disorder (MDD) has been associated with aberrant effective connectivity (EC) among the default mode network (DMN), salience network (SN), and central executive network (CEN)-collectively referred to as triple networks. However, prior research has predominantly concentrated on broad frequency bands (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the influence of distinct rhythms on triple network causal dynamics. In the present study, we aim to investigate EC alterations within the triple networks across various frequency bands in patients with MDD. Utilizing a data-driven frequency decomposition approach and a multivariate Granger causality analysis, we characterized frequency-specific EC patterns of triple networks in 49 MDD patients and 54 healthy controls. A support vector machine classifier was subsequently employed to assess the discriminative capacity of the frequency-specific EC features. Our findings revealed that, compared to controls, patients exhibited not only enhanced mean EC within the CEN in the conventional frequency band (0.01-0.08 Hz), but also decreased mean EC from the SN to the DMN in a higher frequency band (0.12-0.18 Hz), and increased mean EC from the CEN to the SN in a sub-frequency band (0.04-0.08 Hz); the latter was significantly correlated with disease severity. Moreover, optimal classification performance for distinguishing patients from controls was attained by combining EC features across all three frequency bands, with the area under the curve (AUC) value of 0.8831 and the corresponding accuracy, sensitivity, and specificity of 89.97%, 92.63%, and 87.32%, respectively. These insights into EC changes within the triple networks across multiple frequency bands offer valuable perspectives on the neurobiological basis of MDD and could aid in developing frequency-specific EC features as potential biomarkers for disease diagnosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer ZY declared a past co-authorship with the author GL to the handling editor., (Copyright © 2023 Li, Qian, Li and Zhang.)

A test sequence of alternating low-frequency (A) and high-frequency (B) tones in a repeating . . ABAB . . pattern is more likely to be heard as 2 segregated streams of tones when it is preceded by an isofrequency inducer sequence whose frequency matches either the A- or B-tone frequency (e.g., . . BBBB . . .") of the test, a phenomenon referred to as stream biasing. Low-level processes such as stimulus-selective adaptation of frequency-tuned neurons within early auditory processing stages have been thought by some to mediate stream biasing; however, the current study tested for the involvement of higher level processes. Inducers whose frequency matched neither the A- nor B-tone frequency (e.g., ". . . CCCC . . .") sometimes facilitated stream biasing. Stream biasing was also sensitive to complex features of the inducer sequence, namely whether the rhythmic pattern of the inducer matched the rhythm of the ABAB test. Stream biasing occurred even when an 8-s silent interval separated the inducer and test sequences, a time span longer than previously recognized (Beauvois & Meddis, 1997). These results suggest the involvement of persistent activation of high-level representations that affect perception. [ABSTRACT FROM AUTHOR]

Background: Apathy is among the most prevalent and incapacitating non-motor symptoms of Parkinson's disease (PD). PD patients with apathy (PD-A) have been reported to have abnormal spontaneous brain activity mainly in 0.01-0.08 Hz. However, the frequency-dependence of brain activity in PD-A remains unclear. Therefore, this study aimed to examine whether abnormalities in PD-A are associated with specific frequency bands., Materials and Methods: Overall, 28 patients with PD-A, 19 PD patients without apathy (PD-NA), and 32 gender-, age-matched healthy controls (HCs) were enrolled. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data, demographic information, and neuropsychological assessments, including apathy, depression, anxiety and cognitive function for every participant. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) were calculated in the conventional (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) frequency bands based on statistical parametric mapping (SPM12) and RESTplus V1.25. Two-sample t -tests were performed to compare the differences among the three groups., Results: PD-A reduced ALFF in the right anterior cingulate gyri in the slow-5 band and decreased fALFF in the right middle frontal gyrus in the conventional band, compared to patients with PD-NA. However, PerAF, ReHo, and DC could not distinguish PD-A from PD-NA in the three bands. PD-A had higher ALFF and fALFF in the left middle occipital gyrus and lower fALFF in the bilateral insula in the slow-5 band compared to the HCs. Furthermore, abnormal DC value in hippocampus and parahippocampus was observed separately in the conventional band and in the slow-4 band between PD-A and HCs. Moreover, PD-A and PD-NA showed lower ReHo in cerebellum in the three bands compared to the HCs., Conclusion: Our study revealed that PD-A and PD-NA might have different neurophysiological mechanisms. Concurrently, the ALFF in the slow-5 band and fALFF in the conventional band were sensitive in differentiating PD-A from PD-NA. The influence of apathy on the disease can be considered in the future research on PD, with the effects of frequency band taken into account when analyzing spontaneous brain activities in PD-A., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Zhang, Wang, Yang, Chang and Liu.)

Abstract: Stimulus-focused attention enhances the processing of auditory stimuli, which is indicated by enhanced neural activity. In situations where fast responses are required, attention may not only serve as a means to gain more information about the relevant stimulus, but it may provide a processing speed gain as well. In two experiments we investigated whether attentional focusing decreased the latency of the auditory N1 event related potential. In Experiment 1 slowly emerging, soft (20dB sensation level) sounds were presented in two conditions, in which participants performed a sound-detection task or watched a silent movie and ignored the sounds. N1 latency was shorter in the sound-detection task in comparison to the ignore condition. In Experiment 2 we investigated whether the attentional N1 latency-decrease was caused by a frequency-specific attentional preparation or not. To this end, tone sequences were presented with a single tone frequency or with four different frequencies. N1 latency was shorter in the sound-detection task in comparison to the ignore condition regardless the number of frequencies. These results suggest that stimulus-focused attention increases stimulus processing speed by generally increasing sensory gain. [Copyright &y& Elsevier]

Abstract: Flickering light induces visual hallucinations in human observers. Despite a long history of the phenomenon, little is known about the dependence of flicker-induced subjective impressions on the flicker frequency. We investigate this question using Ganzfeld stimulation and an experimental paradigm combining a continuous frequency scan (1–50Hz) with a focus on re-occurring, whole percepts. On the single-subject level, we find a high degree of frequency stability of percepts. To generalize across subjects, we apply two rating systems, (1) a set of complex percept classes derived from subjects’ reports and (2) an enumeration of elementary percept features, and determine distributions of occurrences over flicker frequency. We observe a stronger frequency specificity for complex percept classes than elementary percept features. Comparing the similarity relations among percept categories to those among frequency profiles, we observe that though percepts are preferentially induced by particular frequencies, the frequency does not unambiguously determine the experienced percept. [Copyright &y& Elsevier]

We investigated how physiologically observed forward suppression interacts with stimulus frequency in neuronal responses in the guinea pig auditory cortex. The temporal order and frequency proximity of sounds influence both their perception and neuronal responses. Psychophysically, preceding sounds (conditioners) can make successive sounds (probes) harder to hear. These effects are larger when the two sounds are spectrally similar. Physiological forward suppression is usually maximal for conditioner tones near to a unit's characteristic frequency (CF), the frequency to which a neuron is most sensitive. However, in most physiological studies, the frequency of the probe tone and CF are identical, so the role of unit CF and probe frequency cannot be distinguished. Here, we systemically varied the frequency of the probe tone, and found that the tuning of suppression was often more closely related to the frequency of the probe tone than to the unit's CF, i.e. suppressed tuning was specific to probe frequency. This relationship was maintained for all measured gaps between the conditioner and the probe tones. However, when the probe frequency and CF were similar, CF tended to determine suppressed tuning. In addition, the bandwidth of suppression was slightly wider for off-CF probes. Changes in tuning were also reflected in the firing rate in response to probe tones, which was maximally reduced when probe and conditioner tones were matched in frequency. These data are consistent with the idea that cortical neurons receive convergent inputs with a wide range of tuning properties that can adapt independently. [ABSTRACT FROM AUTHOR]

Several brain regions, including the primary and secondary somatosensory cortices (SI and SII, respectively), are functionally active during the pain experience. Both of these regions are thought to be involved in the sensory-discriminative processing of pain and recent evidence suggests that SI in particular may also be involved in more affective processing. In this study we used MEG to investigate the hypothesis that frequency-specific oscillatory activity may be differentially associated with the sensory and affective components of pain. In eight healthy participants (four male), MEG was recorded during a visceral pain experiment comprising baseline, anticipation, pain and post-pain phases. Pain was delivered via intraluminal oesophageal balloon distension (four stimuli at 1 Hz). Significant bilateral but asymmetrical changes in neural activity occurred in the β-band within SI and SII. In SI, a continuous increase in neural activity occurred during the anticipation phase (20-30 Hz), which continued during the pain phase but at a lower frequency (10-15 Hz). In SII, oscillatory changes only occurred during the pain phase, predominantly in the 20-30 Hz β band, and were coincident with the stimulus. These data provide novel evidence of functional diversity within SI, indicating a role in attentional and sensory aspects of pain processing. In SII, oscillatory changes were predominantly stimulus-related, indicating a role in encoding the characteristics of the stimulus. We therefore provide objective evidence of functional heterogeneity within SI and functional segregation between SI and SII, and suggest that the temporal and frequency dynamics within cortical regions may offer valuable insights into pain processing. [ABSTRACT FROM AUTHOR]

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Electrical acupoint stimulation (EAS) has been used to treat motor dysfunction of stroke patients with reportedly effective results. When we operate EAS treatment, we can modulate the intensity and frequency of stimulation. The purpose of this study is to evaluate the effect of different frequencies in treating motor dysfunction of ischemic stroke patients with EAS. The subjects of this study were 62 ischemic stroke patients with motor dysfunction in Kyunghee oriental medical center. They have been hospitalized after 1 week to 1 month from onset. They were treated with 2 Hz or 120 Hz EAS for 2 weeks, and had motor evoked potentials (MEPs) tests before and after 2 weeks of EAS treatment. We measured latency, central motor conduction time (CMCT) and amplitude of MEPs. After 2 weeks of treatment, we compared MEPs data of the affected side between the 2 Hz group and the 120 Hz group. The 2 Hz group showed more significant improvement than the 120 Hz group in latency, CMCT and amplitude (p = 0.008, 0.002, 0.002). In the case of the affected side MEPs data divided by normal side MEPs data, the 2 Hz group also showed higher improvement rate than the 120 Hz group in latency, CMCT and amplitude with significant differences (p = 0.003, 0.000, 0.008). These results suggest that low frequency EAS activates the central motor conduction system better than high frequency EAS, and EAS with low frequency could be more helpful for motor recovery after ischemic stroke than that with high frequency. [ABSTRACT FROM AUTHOR]

Auditory steady-state responses (ASSR) to amplitude modulated (AM) tones with carrier frequencies between 250 and 4000 Hz and modulation frequencies near 40 Hz were recorded using a 37-channel neuro-magnetometer placed above the auditory cortex contralateral to the stimulated right ear. The ASSR sources were likely in the primary auditory cortex, located more anteriorly and more medially than the N1m sources. The ASSR amplitude decreased with increasing carrier frequency, the amplitude at 250 Hz being three times larger than at 4000 Hz. The amplitude of the ASSR to a test sound decreased in the presence of an interfering second AM sound. This suppression of the ASSR to the test stimulus was greater when the carrier frequency of the interfering stimulus was higher than that of the test tone and was greater when the test stimulus had a lower carrier frequency. Similar frequency specificity was observed when the interfering sound was a non-modulated pure tone. These results differ from those found for the ASSR elicited by modulation frequencies above 80 Hz or for the transient brainstem and middle-latency responses and suggest substantial interactions between phase-locked activities at the level of the primary auditory cortex. [Copyright &y& Elsevier]

Two- or 100-Hz electrical acupoint stimulation (EAS) can induce analgesia via distinct central mechanisms. It has long been known that the extent of EAS analgesia showed tremendous difference among subjects. Functional MRI (fMRI) studies were performed to allocate the possible mechanisms underlying the frequency specificity as well as individual variability of EAS analgesia. In either frequencies, the averaged fMRI activation levels of bilateral secondary somatosensory area and insula, contralateral anterior cingulate cortex and thalamus were positively correlated with the EAS-induced analgesic effect across the subjects. In 2-Hz EAS group, positive correlations were observed in contralateral primary motor area, supplementary motor area, and ipsilateral superior temporal gyrus, while negative correlations were found in bilateral hippocampus. In 100-Hz EAS group, positive correlations were observed in contralateral inferior parietal lobule, ipsilateral anterior cingulate cortex, nucleus accumbens, and pons, while negative correlation was detected in contralateral amygdala. These results suggest that functional activities of certain brain areas might be correlated with the effect of EAS-induced analgesia, in a frequency-dependent dynamic. EAS-induced analgesia with low and high frequencies seems to be mediated by different, though overlapped, brain networks. The differential activations/de-activations in brain networks across subjects may provide a neurobiological explanation for the mechanisms of the induction and the individual variability of analgesic effect induced by EAS, or that of manual acupuncture as well. [Copyright &y& Elsevier]

Rising chirps that compensate for the dispersion of the travelling wave on the basilar membrane evoke larger monaural brainstem responses than clicks [Dau et al., J. Acoust. Soc. Am. 107 (2000) 1530–1540]. In order to test if a similar effect applies for the early processing stages of binaural information, monaurally and binaurally evoked auditory brainstem responses were recorded for clicks and chirps for levels from 10 to 60 dB nHL in steps of 10 dB. Ten thousand sweeps were collected for every stimulus condition from 10 normal hearing subjects. Wave V amplitudes are significantly larger for chirps than for clicks for all conditions. The amplitude of the binaural difference potential, DP1–DN1, is significantly larger for chirps at the levels 30 and 40 dB nHL. Both the binaurally evoked potential and the binaural difference potential exhibit steeper growth functions for chirps than for clicks for levels up to 40 dB nHL. For higher stimulation levels the chirp responses saturate approaching the click evoked amplitude. For both stimuli the latency of DP1 is shorter than the latency of the binaural wave V, which in turn is shorter than the latency of DN1. The amplitude ratio of the binaural difference potential to the binaural response is independent of stimulus level for clicks and chirps. A possible interpretation is that with click stimulation predominantly binaural interaction from high frequency regions is seen which is compatible with a processing by contralateral inhibitory and ipsilateral excitatory (IE) cells. Contributions from low frequencies are negligible since the responses from low frequencies are not synchronized for clicks. The improved synchronization at lower frequencies using chirp stimuli yields contributions from both low and high frequency neurons enlarging the amplitudes of the binaural responses as well as the binaural difference potential. Since the constant amplitude ratio of the binaural difference potential to the binaural response makes contralateral and ipsilateral excitatory interaction improbable, binaural interaction at low frequencies is presumably also of the IE type. Another conclusion of this study is that the chirp stimuli employed here are better suited for auditory brainstem responses and binaural difference potentials than click stimuli since they exhibit higher amplitudes and a better signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

The relations of the stiffness and damping performance to the structure parameters of an active magnetic bearing (AMB) system and the frequency specificity of the control loop are analyzed. The effects of the control current phase on the stability, the stiffness and the damping properties of the system are presented. Meanwhile, a new concept of complex damping coefficient, the practical meanings of some system properties, and the calculation methods are discussed and described. [ABSTRACT FROM AUTHOR]

Copyright of HNO is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effect of frequency on N1m has been investigated by various methods. However, it has not yet been measured using forward masking. In this study, the frequency specificity of N1m was investigated using forward masking. Although the masker frequency had some influence on N1m amplitudes, the results suggested that the frequency specificity of N1m was worse than that of a single-neuron or psychological tuning curve. This is probably because N1m includes various components, both frequency-specific and non-specific, some of which may be less affected by masking. Thus, our results agree with those of previous studies using intervening tones that suggested widespread neural representation in the auditory cortex. [ABSTRACT FROM AUTHOR]