A voxel-level resting-state fMRI study on patients with alcohol use disorders based on a power spectrum slope analysis method

Background

Earlier neuroimaging investigations showed that abnormal brain activity in patients with alcohol use disorder (AUD) was frequency dependent. However, there is lacking of a comprehensive method to capture the amplitude of multi-frequency bands directly. Here, we used a new method, the power spectrum slope (PSS) to explore abnormal spontaneous activity of brain in patients with AUD.

Methods

Thirty-three AUD patients and 29 healthy controls (HCs) enrolled in this study. The coefficient b and the power-law slope b' were calculated and compared between two groups. We also used the receiver operating characteristic (ROC) curve to examine the ability of the PSS analysis to distinguish between AUD and HCs. We next examined the correlation between PSS difference in the brain areas and the severity of alcohol dependence.

Results

Thirty AUD patients and 26 HCs were retained after head motion correction. The two metrics of PSS values increased in the left precentral gyrus in AUD patients. The area under the curve values of PSS differences in the specific brain area were respectively 0.836 and 0.844, with sensitivities of 86.7% and 83.3% and specificities of 73.1% and 76.9%. The Michigan Alcoholism Screening Test (MAST) and Alcohol drinking scale (ADS) scores were not significantly correlated with the PSS values in the specific brain area.

Conclusion

As a novel method, the PSS can well detect abnormal local brain activity in the AUD patients and may offer new insights for future fMRI studies.

Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?

Transfer function analysis (TFA) is the most frequently adopted method for assessing dynamic cerebral autoregulation (CA) with continuously recorded arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). Conventionally, values of autoregulatory metrics (e.g., gain and phase) derived from TFA are averaged within three frequency bands separated by cut-off frequencies at 0.07 Hz and 0.20 Hz, respectively, to represent the efficiency of dynamic CA. However, this is of increasing concerns, as there remains no solid evidence for choosing these specific cut-off frequencies, and the rigid adoption of these bands can stifle further developments in TFA of dynamic CA. In this 'Point-Counterpoint' mini-review, we provide evidence against the fixed banding, indicate possible alternatives, and call for awareness of the risk of the 'one-size-fits-all' banding becoming dogmatic. We conclude that we need to remain open to the multiple possibilities offered by TFA to realize its full potential in studies of human dynamic CA.

Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?

Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) is based on linear system theory to examine the relationship between changes in blood pressure and cerebral blood flow. With TFA, dCA is characterized as a frequency-dependent phenomenon quantified by gain, phase, and coherence in the distinctive frequency bands. These frequency bands likely reflect the underlying regulatory mechanisms of the cerebral vasculature. In addition, obtaining TFA metrics over a specific frequency band facilitates reliable spectral estimation and statistical data analysis to reduce random noise. This commentary discusses the benefits and cautions of banding TFA parameters in dCA studies.

A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications

In-body biomedical devices (IBBDs) are receiving significant attention in the discovery of solutions to complex medical conditions. Biomedical devices, which can be ingested, injected or implanted in the human body, have made it viable to screen the physiological signs of a patient wirelessly, without regular hospital appointments and routine check-ups, where the antenna is a mandatory element for transferring bio-data from the IBBDs to the external world. However, the design of an in-body antenna is challenging due to the dispersion of the dielectric constant of the tissues and unpredictability of the organ structures of the human body, which can absorb most of the antenna radiation. Therefore, various factors must be considered for an in-body antenna, such as miniaturization, link budget, patient safety, biocompatibility, low power consumption and the ability to work effectively within acceptable medical frequency bands. This paper presents a comprehensive overview of the major facets associated with the design and challenges of in-body antennas. The review comprises surveying the design specifications and implementation methodology, simulation software and testing of in-body biomedical antennas. This work aims to summarize the recent in-body antenna innovations for biomedical applications and indicates the key research challenges.

Improved HHT-microstate analysis of EEG in nicotine addicts

Background

Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease.

Methods

To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts.

Results

After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods.

Conclusion

Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

Wavelet analysis of laser Doppler microcirculatory signals: Current applications and limitations

Laser Doppler flowmetry (LDF) has long been considered a gold standard for non-invasive assessment of skin microvascular function. Due to the laser Doppler (LD) microcirculatory signal's complex biological and physiological context, using spectral analysis is advisable to extract as many of the signal's properties as feasible. Spectral analysis can be performed using either a classical Fourier transform (FT) technique, which has the disadvantage of not being able to localize a signal in time, or wavelet analysis (WA), which provides both the time and frequency localization of the inspected signal. So far, WA of LD microcirculatory signals has revealed five characteristic frequency intervals, ranging from 0.005 to 2 Hz, each of which being related to a specific physiological influence modulating skin microcirculatory response, providing for a more thorough analysis of the signals measured in healthy and diseased individuals. Even though WA is a valuable tool for analyzing and evaluating LDF-measured microcirculatory signals, limitations remain, resulting in a lack of analytical standardization. As a more accurate assessment of human skin microcirculation may better enhance the prognosis of diseases marked by microvascular dysfunction, searching for improvements to the WA method is crucial from the clinical point of view. Accordingly, we have summarized and discussed WA application and its limitations when evaluating LD microcirculatory signals, and presented insight into possible future improvements. We adopted a novel strategy when presenting the findings of recent studies using WA by focusing on frequency intervals to contrast the findings of the various studies undertaken thus far and highlight their disparities.

Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification

Background

Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding.

Objective

This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction.

Methods

The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method.

Results

The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time.

Conclusion

These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.

Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation

Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.

Reliability of Upper Limb Pin-Prick Stimulation With Electroencephalography: Evoked Potentials, Spectra and Source Localization

In order for electroencephalography (EEG) with sensory stimuli measures to be used in research and neurological clinical practice, demonstration of reliability is needed. However, this is rarely examined. Here we studied the test-retest reliability of the EEG latency and amplitude of evoked potentials and spectra as well as identifying the sources during pin-prick stimulation. We recorded EEG in 23 healthy older adults who underwent a protocol of pin-prick stimulation on the dominant and non-dominant hand. EEG was recorded in a second session with rest intervals of 1 week. For EEG electrodes Fz, Cz, and Pz peak amplitude, latency and frequency spectra for pin-prick evoked potentials was determined and test-retest reliability was assessed. Substantial reliability ICC scores (0.76-0.79) were identified for evoked potential negative-positive amplitude from the left hand at C4 channel and positive peak latency when stimulating the right hand at Cz channel. Frequency spectra showed consistent increase of low-frequency band activity (< 5 Hz) and also in theta and alpha bands in first 0.25 s. Almost perfect reliability scores were found for activity at both low-frequency and theta bands (ICC scores: 0.81-0.98). Sources were identified in the primary somatosensory and motor cortices in relation to the positive peak using s-LORETA analysis. Measuring the frequency response from the pin-prick evoked potentials may allow the reliable assessment of central somatosensory impairment in the clinical setting.

Frequency-specific age-related changes in the amplitude of spontaneous fluctuations in autism

BACKGROUND

Autism spectrum disorder is characterized by atypical developmental changes during brain maturation, but regional brain functional changes that occur with age and across different frequency bands are unknown. Therefore, the current study aimed to explore potential age and frequency band-related changes in the regional brain activities in autism.

METHODS

A total of 65 participants who met the DSM-IV criteria for autistic disorder and 55 typically developed (TD) participants (both age 6-30 years) were recruited in the current study. The two groups were matched in age (t=-1.314, P=0.191) and gender (χ²=2.760, P=0.097). The amplitude of low-frequency fluctuations (ALFF) was employed to explore the effect of development on spontaneous brain activity in individuals with autism and in TD participants across slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and slow-3 (0.073-0.1 Hz) frequency bands. The diagnosis-by-age interaction effect in the whole brain voxels in autism and TD groups was investigated.

RESULTS

Autism individuals showed significantly higher ALFF in the dorsal striatum in childhood (Caudate cluster: t=3.626, P=0.001; Putamen cluster: t=2.839, P=0.007) and remarkably lower ALFF in the dorsal striatum in adulthood (Caudate cluster: t=-2.198, P=0.038; Putamen cluster: t=-2.314, P=0.030) relative to TD, while no significant differences were observed in adolescence (all P>0.05). In addition, abnormal ALFF amplitudes were specific to the slow-4 (0.027-0.073 Hz) frequency band in the clusters above.

CONCLUSIONS

The current study indicated abnormal development patterns in the spontaneous activity of the dorsal striatum in autism and highlighted the potential role of the slow-4 frequency band in the pathology of autism. Also, the potential brain mechanism of autism was revealed, suggesting that autism-related variations should be investigated in a specific frequency.

The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

Brain Frequency-Specific Changes in the Spontaneous Neural Activity Are Associated With Cognitive Impairment in Patients With Presbycusis

Presbycusis (PC) is characterized by preferential hearing loss at high frequencies and difficulty in speech recognition in noisy environments. Previous studies have linked PC to cognitive impairment, accelerated cognitive decline and incident Alzheimer’s disease. However, the neural mechanisms of cognitive impairment in patients with PC remain unclear. Although resting-state functional magnetic resonance imaging (rs-fMRI) studies have explored low-frequency oscillation (LFO) connectivity or amplitude of PC-related neural activity, it remains unclear whether the abnormalities occur within all frequency bands or within specific frequency bands. Fifty-one PC patients and fifty-one well-matched normal hearing controls participated in this study. The LFO amplitudes were investigated using the amplitude of low-frequency fluctuation (ALFF) at different frequency bands (slow-4 and slow-5). PC patients showed abnormal LFO amplitudes in the Heschl’s gyrus, dorsolateral prefrontal cortex (dlPFC), frontal eye field and key nodes of the speech network exclusively in slow-4, which suggested that abnormal spontaneous neural activity in PC was frequency dependent. Our findings also revealed that stronger functional connectivity between the dlPFC and the posterodorsal stream of auditory processing, as well as lower functional coupling between the PCC and key nodes of the DMN, which were associated with cognitive impairments in PC patients. Our study might underlie the cross-modal plasticity and higher-order cognitive participation of the auditory cortex after partial hearing deprivation. Our findings indicate that frequency-specific analysis of ALFF could provide valuable insights into functional alterations in the auditory cortex and non-auditory regions involved in cognitive impairment associated with PC.

Frequency Band Characteristics of a Triboelectric Nanogenerator and Ultra-Wide-Band Vibrational Energy Harvesting

Micromechanical vibration, as one of the most prevalent forms of energy in an ambient environment, has surpassing application potentials as the power source for self-powered electronics. A triboelectric nanogenerator (TENG) can effectively convert vibrational energy to electricity, which has the unique benefit of a wide-band over a traditional vibration energy harvester due to the contact electrification mechanism. Herein, the frequency band characteristics of vibrational TENG (V-TENG) were systematically elaborated. The mechanical model of V-TENG was established to explore its working mechanism for wide-band vibrational energy harvesting. By simulation analysis and experimental validation, the bandwidth dependence of V-TENG on acceleration magnitude, proof mass, stiffness, and gap distance was investigated in detail. With optimized structural parameters, an ultra-wide-band vibration energy harvester (UVEH) was developed by a tandem spring-mass structure. Within the ultra-wide-band range from 3 to 45 Hz, the UVEH can invariably illuminate 36 serial light-emitting diodes (LEDs) and charge a 33 μF capacitor to 1.5 V within 35 s. This work has quantitatively studied frequency band characteristics of V-TENG and provided a promising strategy for wide-band vibrational energy harvesting from a machine, bridge, water wave, and human motion.

Frequency‑Specific Regional Homogeneity Alterations and Cognitive Function in Obstructive Sleep Apnea Before and After Short-Term Continuous Positive Airway Pressure Treatment

PURPOSE

Previous studies have demonstrated abnormal local spontaneous brain activity in the conventional frequency bands (0.01-0.08 Hz) in obstructive sleep apnea (OSA). However, it is not clear whether these abnormalities are associated with the specific frequency band of low-frequency oscillations or whether it can be improved with a continuous positive airway pressure (CPAP) treatment. This study aimed to investigate the regional homogeneity (ReHo) in specific frequency at baseline (pre-CPAP) and after one month of CPAP adherence treatment (post-CPAP) in OSA patients.

METHODS

Twenty-one patients with moderate-to-severe OSA and 21 age- and sex-matched healthy controls (HCs) were included in the final analysis. ReHo was calculated in three different frequency bands (typical frequency band: 0.01-0.1 Hz; slow-5 band: 0.01-0.027 Hz; slow-4 band: 0.027-0.073 Hz), respectively. A partial correlational analysis was performed to assess the relationship between altered ReHo and clinical evaluation.

RESULTS

OSA patients revealed increased ReHo in the brainstem, bilateral inferior temporal gyrus (ITG)/fusiform, and right-cerebellum posterior lobe (CPL), and decreased ReHo in the bilateral inferior parietal lobule (IPL), right superior temporal gyrus (STG), and left precentral gyrus (PG) compared to HC groups in different frequency bands. Significantly changed ReHo in the bilateral middle temporal gyrus (MTG), PG, medial frontal gyrus (MFG), supplementary motor area (SMA), CPL, IPL, left superior frontal gyrus (SFG), ITG, MTG, and right STG were observed between post-CPAP and pre-CPAP OSA patients, which was associated with specific frequency bands. The altered ReHo in specific frequency bands was correlated with Montreal cognitive assessment score, Epworth sleepiness scale, and apnea hypopnea index in pre-CPAP OSA patients.

CONCLUSION

These findings indicate that OSA has frequency-related abnormalities of spontaneous neural activity before and after short-term CPAP treatment, which might contribute to a better understanding of local neural psychopathology and may serve as potential biomarkers for clinical CPAP treatment.

Enhanced Quarter Spherical Acoustic Energy Harvester Based on Dual Helmholtz Resonators

An enhanced quarter-spherical acoustic energy harvester (AEH) with dual Helmholtz resonators was designed in this work. Compared with the previous research, this AEH can harvest multi-directional acoustic energy, has a widened resonance frequency band, and has an improved energy conversion efficiency. When the length of resonator's neck is changed, the acoustic resonant frequency of the two resonators is different. The theoretical models of output voltage and output power were studied, and the relationship of output performance with frequency was obtained. The results showed that this AEH can operate efficiently in a frequency band of about 470 Hz. Its output voltage was found to be about 28 mV, and its output power was found to be about 0.05 μW. The power density of this AEH was found to be about 12.7 µW/cm². Therefore, this AEH could be widely used in implantable medical devices such as implantable cardiac pacemakers, cochlear implants, and retinal prosthesis.

Aberrant Amplitude of Low-Frequency Fluctuations in Different Frequency Bands in Patients With Parkinson's Disease

Previous studies reported abnormal spontaneous neural activity in Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging (R-fMRI). However, the frequency-dependent neural activity in PD is largely unknown. Here, 35 PD patients and 35 age- and education-matched healthy controls (HCs) underwent R-fMRI scanning to investigate abnormal spontaneous neural activity of PD using the amplitude of low-frequency fluctuation (ALFF) approach within the conventional band (typical band: 0.01-0.08 Hz) and specific frequency bands (slow-5: 0.010-0.027 Hz and slow-4: 0.027-0.073 Hz). Compared with HCs, PD patients exhibited increased ALFF in the parieto-temporo-occipital regions, such as the bilateral inferior temporal gyrus/fusiform gyrus (ITG/FG) and left angular gyrus/posterior middle temporal gyrus (AG/pMTG), and displayed decreased ALFF in the left cerebellum, right precuneus, and left postcentral gyrus/supramarginal gyrus (PostC/SMG) in the typical band. PD patients showed greater increased ALFF in the left caudate/putamen, left anterior cingulate cortex/medial superior frontal gyrus (ACC/mSFG), left middle cingulate cortex (MCC), right ITG, and left hippocampus, along with greater decreased ALFF in the left pallidum in the slow-5 band, whereas greater increased ALFF in the left ITG/FG/hippocampus accompanied by greater decreased ALFF in the precentral gyrus/PostC was found in the slow-4 band (uncorrected). Additionally, the left caudate/putamen was positively correlated with levodopa equivalent daily dose (LEDD), Hoehn and Yahr (HY) stage, and disease duration. Our results suggest that PD is related to widespread abnormal brain activities and that the abnormalities of ALFF in PD are associated with specific frequency bands. Future studies should take frequency band effects into account when examining spontaneous neural activity in PD.

Dynamic Functional Connectivity Strength Within Different Frequency-Band in Schizophrenia

As a complex psychiatric disorder, schizophrenia is interpreted as a "dysconnection" syndrome, which is linked to abnormal integrations in between distal brain regions. Recently, neuroimaging has been widely adopted to investigate how schizophrenia affects brain networks. Furthermore, some studies reported frequency dependence of the abnormalities of functional network in schizophrenia, however, dynamic functional connectivity with frequency dependence is rarely used to explore changes in the whole brain of patients with schizophrenia (SZ). Therefore, in the current study, dynamic functional connectivity strength (dFCS) was performed on resting-state functional magnetic resonance data from 96 SZ patients and 121 healthy controls (HCs) at slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz) frequency bands and further assessed whether the altered dFCS was correlated to clinical symptoms in SZ patients. Results revealed that decreased dFCS of schizophrenia were found in salience, auditory, sensorimotor, visual networks, while increased dFCS in cerebellum, basal ganglia, and prefrontal networks were observed across different frequency bands. Specifically, the thalamus subregion of schizophrenic patients exhibited enhanced dynamic FCS in slow-5 and slow-4, while reduced in slow-3. Moreover, in slow-5 and slow-4, significant interaction effects between frequency and group were observed in the left calcarine cortex, the bilateral inferior orbitofrontal gyrus, and anterior cingulum cortex (ACC). Furthermore, the altered dFCS of insula, thalamus (THA), calcarine cortex, orbitofrontal gyrus, and paracentral lobule were partial correlated with clinical symptoms of SZ patients in slow-5 and slow-4 bands. These results demonstrate the abnormalities of dFCS in schizophrenia patients is rely on different frequency bands and may provide potential implications for exploring the neuropathological mechanism of schizophrenia.

Frequency-Specific Abnormalities Of Functional Homotopy In Alcohol Dependence: A Resting-State Functional Magnetic Resonance Imaging Study

PURPOSE

Alcohol dependence (AD) is a relapsing mental disorder, typically occurring with concurrent tobacco misuse. Studies have reported disruption of the structural connectivity between hemispheres in the brain of individuals with AD. However, alterations in interhemispheric interactions and the specificity of frequency bands in individuals with AD remain unknown. Voxel-mirrored homotopic connectivity (VMHC) allows examination of functional interactions between mirrored interhemispheric voxels. Here, we use VMHC to investigate homotopic connectivity in AD and alcohol and nicotine co-dependence (AND) subjects.

PATIENTS AND METHODS

VMHC and seed-based functional connectivity (FC) in 24 AD, 30 AND, and 35 sex-, age-, and education-matched healthy control (HC) subjects were calculated for different frequency bands (slow-5, slow-4, and typical bands).

RESULTS

Individuals with AD demonstrated significantly reduced VMHC in bilateral cerebellum posterior lobe (CPL) and increased VMHC in bilateral middle frontal gyrus (MFG) compared to that in HCs in the typical and slow-4 bands; higher VMHC in the MFG was positively correlated with the dependence-severity score. In all bands of the VMHC analysis, no significant differences were found between the AND and other groups. Subsequent seed-based FC analysis demonstrated all regions with abnormal VMHC exhibited altered FC with its counterpart in the contralateral hemisphere in the typical and slow-4 frequency bands. The FC value between bilateral CPL within AD subjects negatively correlated with alcohol intake.

CONCLUSION

Our findings provide further evidence of the role of disruptions within the brain circuitry supporting cognitive control in the development of AD. Alterations in neural activities in the CPL and MFG might be a biomarker of dependence severity in AD patients as assessed using clinical questionnaire and features. Because of the frequency specificity in VMHC, we must consider frequency effects in future AD functional magnetic resonance imaging studies.

EEG Bands of Wakeful Rest, Slow-Wave and Rapid-Eye-Movement Sleep at Different Brain Areas in Rats

Accumulating evidence reveals that neuronal oscillations with various frequency bands in the brain have different physiological functions. However, the frequency band divisions in rats were typically based on empirical spectral distribution from limited channels information. In the present study, functionally relevant frequency bands across vigilance states and brain regions were identified using factor analysis based on 9 channels EEG signals recorded from multiple brain areas in rats. We found that frequency band divisions varied both across vigilance states and brain regions. In particular, theta oscillations during REM sleep were subdivided into two bands, 5–7 and 8–11 Hz corresponding to the tonic and phasic stages, respectively. The spindle activities of SWS were different along the anterior-posterior axis, lower oscillations (~16 Hz) in frontal regions and higher in parietal (~21 Hz). The delta and theta activities co-varied in the visual and auditory cortex during wakeful rest. In addition, power spectra of beta oscillations were significantly decreased in association cortex during REM sleep compared with wakeful rest. These results provide us some new insights into understand the brain oscillations across vigilance states, and also indicate that the spatial factor should not be ignored when considering the frequency band divisions in rats.

Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.

Amplitude of Low-Frequency Fluctuations in Multiple-Frequency Bands in Acute Mild Traumatic Brain Injury

Functional disconnectivity during the resting state has been observed in mild traumatic brain injury (mTBI) patients during the acute stage. However, it remains largely unknown whether the abnormalities are related to specific frequency bands of the low-frequency oscillations (LFO). Here, we used the amplitude of low-frequency fluctuations (ALFF) to examine the amplitudes of LFO in different frequency bands (slow-5: 0.01–0.027 Hz; slow-4: 0.027–0.073 Hz; and typical: 0.01–0.08 Hz) in patients with acute mTBI. A total of 24 acute mTBI patients and 24 age-, sex-, and education-matched healthy controls participated in this study. In the typical band, acute mTBI patients showed lower standardized ALFF in the right middle frontal gyrus and higher standardized ALFF in the right lingual/fusiform gyrus and left middle occipital gyrus. Further analyses showed that the difference between groups was concentrated in a narrower (slow-4) frequency band. In the slow-5 band, mTBI patients only exhibited higher standardized ALFF in the occipital areas. No significant correlation between the mini-mental state examination score and the standardized ALFF value was found in any brain region in the three frequency bands. Finally, no significant interaction between frequency bands and groups was found in any brain region. We concluded that the abnormality of spontaneous brain activity in acute mTBI patients existed in the frontal lobe as well as in distributed brain regions associated with integrative, sensory, and emotional roles, and the abnormal spontaneous neuronal activity in different brain regions could be better detected by the slow-4 band. These findings might contribute to a better understanding of local neural psychopathology of acute mTBI. Future studies should take the frequency bands into account when measuring intrinsic brain activity of mTBI patients.

Frequency-dependent neural activity in Parkinson's disease

The brainstem and basal ganglia are important in the pathophysiology of Parkinson's disease (PD). Reliable and sensitive detection of neural activity changes in these regions should be helpful in scientific and clinical research on PD. In this study, we used resting state functional MRI and amplitude of low frequency fluctuation (ALFF) methods to examine spontaneous neural activity in 109 patients with PD. We examined activity in two frequency bands, slow-4 (between 0.027 and 0.073 Hz) and slow-5 (0.010-0.027 Hz). Patients had decreased ALFF in the striatum and increased ALFF in the midbrain, and changes were more significant in slow-4. Additionally, changes in slow-4 in both basal ganglia and midbrain correlated with the severity of the parkinsonism. The ALFF in the caudate nucleus positively correlated with the dose of levodopa, while the ALFF in the putamen negatively correlated with the disease duration in both slow-4 and slow-5 bands. In addition, the ALFF in the rostral supplementary motor area negatively correlated with bradykinesia subscale scores. Our findings show that with a large cohort of patients and distinguishing frequency bands, neural modulations in the brainstem and striatum in PD can be detected and may have clinical relevance. The physiological interpretation of these changes needs to be determined.