Sleep deprivation changes frequency-specific functional organization of the resting human brain.

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.

Terahertz Photons Inhibit Cancer Cells Long Term by Suppressing Nano Telomerase Activity.

Telomeres are nanoscale DNA-protein complexes to protect and stabilize chromosomes. The reexpression of telomerase in cancer cells is a key determinant crucial for the infinite proliferation and long-term survival of most cancer cells. However, the use of telomerase inhibitors for cancer treatment may cause problems such as poor specificity, drug resistance, and cytotoxicity. Here, we discovered a nondrug and noninvasive terahertz modulation strategy capable of the long-term suppression of cancer cells by inhibiting telomerase activity. First, we found that an optimized frequency of 33 THz photon irradiation effectively inhibited the telomerase activity by molecular dynamics simulation and frequency filtering experiments. Moreover, in vitro experiments showed that telomerase activity in 4T1 and MCF-7 cells significantly decreased by 77% and 80% respectively, after 21 days of regular 33 THz irradiation. Furthermore, two kinds of cells were found to undergo aging, apoptosis, and DNA double-strand breaks caused by telomere crisis, which seriously affected the survival of cancer cells. In addition, the tumorigenicity of 4T1 cells irradiated with 33 THz waves for 21 days in in vivo mice decreased by 70%. In summary, this study demonstrates the potential application of THz modulation in nano therapy for cancer.

Adjustment of rotation and saturation effects (AROSE) for CEST imaging.

PURPOSE: Endogenous CEST signal usually has low specificity due to contaminations from the magnetization transfer contrast (MTC) and other labile protons with overlapping or close Larmor frequencies. We propose to improve CEST signal specificity with adjustment of rotation and saturation effects (AROSE). METHODS: The AROSE approach measures the difference between CEST signals acquired with the same average irradiation power but largely different duty cycles, for example, a continuous wave or a high duty cycle pulse train versus a low duty cycle pulse train with a flip angle φ. Simulation, phantom, and in vivo rodent studies were performed to evaluate the characteristics of the AROSEφ signal. RESULTS: Simulation and experimental results show that AROSE2π is a low-pass filter that can suppress fast exchanging processes (e.g., >3000 s-1 ), whereas AROSEπ is a band-pass filter suppressing both fast and slow exchange (e.g., <30 s-1 ) rates. For other φ angles, the sensitivity and the exchange-rate filtering effect of AROSEφ falls between AROSEπ and AROSE2π . AROSE can also minimize MTC and improve the Larmor frequency selectivity of the CEST signal. The linewidth of the AROSE1.5π spectrum is about 60% to 65% when compared to the CEST spectrum measured by continuous wave. Depending on the needs of an application, the sensitivity, exchange-rate filtering, and Larmor frequency selectivity can be adjusted by varying the flip angle, duty cycle, and average irradiation power. CONCLUSION: Compared to conventional CEST signals, AROSE can minimize MTC and improve exchange rate filtering and Larmor frequency specificity.

Aberrant dynamic and static functional connectivity of the striatum across specific low-frequency bands in patients with autism spectrum disorder.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Theoretical Considerations for Optimizing the Use of Optogenetics with Complex Behavior.

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.

Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study.

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.

Tinnitus-frequency specific activity and connectivity: A MEG study.

Tinnitus pathophysiology has been associated with an atypical cortical network that involves functional changes in auditory and non-auditory areas. Numerous resting-state studies have replicated a tinnitus brain network to be significantly different from healthy-controls. Yet it is still unknown whether the cortical reorganization is attributed to the tinnitus frequency specifically or if it is frequency-irrelevant. Employing magnetoencephalography (MEG), the current study aimed to identify frequency-specific activity patterns by using an individual tinnitus tone (TT) and a 500 Hz-control tone (CT) as auditory stimuli, across 54 tinnitus patients. MEG data were analyzed in a data-driven approach employing a whole-head model in source space and in sources' functional connectivity. Compared to the CT, the event related source space analysis revealed a statistically significant response to TT involving fronto-parietal regions. The CT mainly involved typical auditory activation-related regions. A comparison of the cortical responses to a healthy control group that underwent the same paradigm rejected the alternative interpretation that the frequency-specific activation differences were due to the higher frequency of the TT. Overall, the results suggest frequency-specificity of tinnitus-related cortical patterns. In line with previous studies, we demonstrated a tinnitus-frequency specific network comprising left fronto-temporal, fronto-parietal and tempo-parietal junctions.

Effects of transcranial alternating current stimulation on motor performance and motor learning for healthy individuals: A systematic review and meta-analysis.

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. Systematic Review Registration: PROSPERO, identifier CRD42022342884.

Abnormal brain activities in multiple frequency bands in Parkinson's disease with apathy.

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.

Investigating the spectral features of the brain meso-scale structure at rest.

Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core-peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.

Changes in Degree Centrality of Network Nodes in Different Frequency Bands in Parkinson's Disease With Depression and Without Depression.

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.

Electrical Cochlear Response as an Objective Measure of Hearing Threshold and Hearing Performance Evaluation in Pediatric Cochlear Implant Users / Respuesta Coclear Eléctrica como Medida Objetiva del Umbral Auditivo y la Evaluación del Rendimiento Auditivo en Usuarios Pediátricos de Implante Coclear

ABSTRACT The difficulties of applying the audiometry in pediatric populations and its methodological limitations in implanted patients have spurred the development of new alternative auditory evaluation methods. This study aimed to show an objective method to estimate hearing thresholds in pediatric cochlear implanted patients through Electrical Cochlear Response (ECR) and to quantify the hearing performance by using an Auditory Skills Questionnaire (ASQ) and a Calibrated Sounds Test (CST) designed on purpose. Eighteen implanted patients, 1-6 years old underwent standard audiometry, ECR, and ASQ in two evaluation sessions T1 and T2. At T2, in addition, patients underwent CST. For patients ≤3 years old (G1), Pure Tone Averages (PTA and PTAECR)showed a statistically significant difference between them at T1 and T2. At T2 improvements in audiometric and ECR thresholds were observed (p<0.05), regarding T1. Patients older than 3 years (G2) had significantly better ASQ and CST scores. CST detection scores at 40 dBHL for groups G1 and G2, 36% and 70% respectively, showed a better relationship to ECR thresholds. The relationship observed between ECR thresholds and CST detection scores seems to confirm that ECR brings the feasibility of objective hearing threshold estimation and provides a better frequency resolution than audiometry.

RESUMEN Las dificultades para la aplicación de la audiometría en la población pediátrica además de sus limitaciones metodológicas en pacientes usuarios de implante coclear, señalan la necesidad de métodos audiométricos alternos. En el presente trabajo se utiliza el potencial eléctrico, denominado Respuesta Coclear Eléctrica (ECR) observado solamente en usuarios de implante coclear, para la estimación de umbrales auditivos prescindiendo de la participación consiente del paciente, además de evaluar el desempeño auditivo mediante un Cuestionario de Habilidades Auditivas (ASQ) y la Prueba de Sonidos Calibrados (CST). A dieciocho participantes de 1 a 6 años, se les practicó Audiometría, ECR y ASQ en dos sesiones, T1 y T2; adicionalmente, en T2 se aplicó CST. En T1 y T2 los promedios de tonos puros, PTA y PTAECR, de pacientes ≤ 3 años (G1), mostraron una diferencia estadísticamente significativa entre ellos. En T2 los umbrales audiométricos y ECR (p <0.05), mejoraron respecto de T1. Pacientes > 3 años (G2) lograron puntuaciones ASQ y CST significativamente mejores. Los puntajes de detección CST a 40 dBHL, G1(36%) y G2(70%), mostraron mejor relación con los umbrales ECR. Esta relación entre los umbrales ECR y los puntajes de detección CST indican que la ECR permite estimar el umbral de audición, logrando adicionalmente mayor resolución en frecuencia que la audiometría.

Frequency-Specific Changes of Resting Brain Activity in Parkinson's Disease: A Machine Learning Approach.

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.

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks.

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.

Investigating potential interactions between envelope following responses elicited simultaneously by different vowel formants.

Envelope following responses (EFRs) evoked by the periodicity of voicing in vowels are elicited at the fundamental frequency of voice (f0), 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 f0 selectively in one or more formants. The harmonics contributing to an EFR can then be differentiated by the unique f0 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.

The onset and post-onset auditory responses of cochlear nucleus neurons are modulated differently by cortical activation.

Auditory cortex exhibit a capacity of modulating the functions of subcortical auditory nuclei and even inner ear through descending pathways. The cochlear nucleus (CN), which acts as the gateway from the auditory periphery to the central auditory system, is also subjected to corticofugal modulation. Cortical modulation of subcortical nuclei is highly specific to the frequency tunings of cortical and subcortical neurons. It is unclear whether the high frequency-specificity of the cortical modulation of CN frequency tuning is implemented in the CN, in the auditory periphery, or in both. We analyzed the corticofugal effects on the frequency tuning, constructed from both onset (OS) and post-onset (pOS) response components of CN neurons in C57 mice. We found that the focal electrical stimulation of the primary auditory cortex (ESAI) induced remarkable changes in the response magnitude, response latency and the frequency response curves of CN neurons. The changes in the pOS components were highly specific to the difference in BFs between the stimulated AI neurons and recorded CN neurons. The changes in the OS component mostly involved the augmentation of the auditory responses of CN neurons, while exhibiting far poorer frequency-specificity. Considering the large differences in the temporal response patterns and the tuning shapes between the auditory nerve (AN) and the CN, our data suggest that the CN intrinsic neural circuitry plays a critical role in the frequency specificity of corticofugal modulation. Cortical modulation of the inner ear mostly contributes to the augmentation of the AN inputs to the CN, around the BFs of stimulated AI neurons.

Frequency specificity of amplitude envelope patterns in noise-vocoded speech.

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.

Multiscale energy reallocation during low-frequency steady-state brain response.

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.

Frequency dependent hub role of the dorsal and ventral right anterior insula.

The right anterior insula (rAI) plays a crucial role in generating adaptive behavior by orchestrating multiple brain networks. Based on functional separation findings of the insula and spectral fingerprints theory of cognitive functions, we hypothesize that the hub role of the rAI is region and frequency dependent. Using the Human Connectome Project dataset and backtracking approach, we segregate the rAI into dorsal and ventral parts at frequency bands from slow 6 to slow 3, indicating the frequency dependent functional separation of the rAI. Functional connectivity analysis shows that, within lower than 0.198 Hz frequency range, the dorsal and ventral parts of rAI form a complementary system to synchronize with externally and internally-oriented networks. Moreover, the relationship between the dorsal and ventral rAIs predicts the relationship between anti-correlated networks associated with the dorsal rAI at slow 6 and slow 5, suggesting a frequency dependent regulation of the rAI to brain networks. These findings could improve our understanding of the rAI by supporting the region and frequency dependent function of rAI and its essential role in coordinating brain systems relevant to internal and external environments.

Intrinsic frequency specific brain networks for identification of MCI individuals using resting-state fMRI.

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.