人类听觉皮层的频率选择性功能连通性研究

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"这篇研究论文探讨了功能连通性在人类听觉皮层中的表现，特别是与音调异位（tonotopic differentiation）的关系。研究人员来自西南大学的电子与信息工程学院、心理学学院、情感计算与信息处理研究所、非线性电路与智能信息处理重庆市重点实验室、重庆脑科学协同创新中心以及音乐学院。主要通讯作者是刘光元，邮箱为liugy@swu.edu.cn。论文摘要指出，最近的研究已经证实，人类听觉皮层（HAC）在静息状态下的功能连通性（RS-FC）具有频率选择性。然而，HAC之间以及与大脑其他区域的RS-FC是否与音调空间结构有关尚不清楚。音调异位是指听觉皮层中对不同频率声音的敏感性分布，这种分布是高度组织化的，反映了声音频率的空间编码。该研究可能通过使用功能性磁共振成像（fMRI）技术，分析静息状态下大脑各区域之间的信号同步性，以揭示与音调处理相关的神经网络模式。功能连通性是指在没有特定任务执行时，大脑不同部位之间存在的统计相关性，这反映了它们在处理信息时的潜在协同作用。研究可能涉及多个步骤：首先，通过fMRI扫描收集参与者的大脑活动数据；其次，利用数据分析方法（如连接组学分析）识别HAC内部和与其他脑区的连接模式；然后，将这些连接模式与已知的音调异位图谱进行比较，探索是否存在频率依赖的连通性模式。此外，研究可能还会关注不同个体间的差异，以及这些差异如何影响音调感知能力。结果可能有助于理解大脑如何处理复杂的声音信息，特别是在音乐和语言理解中的关键作用。这将为听觉系统疾病，如听力损失、耳鸣或神经退行性疾病的理解提供新的见解，并可能推动更有效的康复策略。此外，这些发现也可能为开发人工智能和机器学习算法以模拟人类听觉处理提供理论基础。这篇论文的核心目标是揭示人类听觉皮层的功能连通性如何反映和影响音调异位的神经机制，从而增进我们对听觉认知和大脑网络的理解。"

an “expectation effect” in the task-based experiment, the six different

tone stimuli and quiet baseline stimuli were presented in an alternating

pseudo random pattern. The intensity of all stimuli was 75 dB, but due

to differences between individuals, participants could adjust the loud-

ness of the tone bursts between 70 and 80 dB.

2.3

Procedure

For each subject, three types of MRI data were acquired on the same

day: resting-state fMRI, task-based fMRI, and T1 image data. To elimi-

nate any possible influence of task execution, resting data were col-

lected prior to task-based data collection, which was then followed by

high-resolution T1-image data collection (Figure 1). To improve the sta-

tistical effectiveness of each single subject, multiple task-based fMRI

runs were used to increase the total number of trials. During the audi-

tory task, participants were only asked to stay awake and passively lis-

ten to the stimuli (Cha et al., 2016; Hu et al., 2017). The stimulus

sequence was played via MR-compatible high-fidelity headphones

(Optoacoustics) that used an adaptive DSP-based noise-reduction

filtering algorithm.

All images were collected on a 3.0-T Siemens (Erlangen, Germany)

scanner. The resting-state data were collected using an echo-planar

imaging (EPI) sequence (FOV 5 220 3 220 mm, acquisition

matrix 5 64 3 64 mm, pixel spacing 5 3.43753 3.4375 mm, slice

thickness 5 3.0 mm, TR 5 2000 ms, TE 5 30 ms, flip angle 5 908). The

task-based functional images were sampled using a 10-min EPI

sequence (FOV 5 220 3 220 mm, acquisition matrix 5 64 3 64 mm,

pixel spacing 5 3.4375 3 3.4375 mm, slice thickness 5 3.0 mm,

TR 5 3400 ms, TE 5 26 ms, flip angle 5 908). High spatial resolution

3D-T1 anatomical data were gathered using a magnetization-prepared

rapid gradient-echo (MPRAGE) sequence (FOV 5 246 3 256 mm,

acquisition matrix 5 246 3 256, pixel spacing5 1 3 1 mm, slice

thickness 5 1mm,TR5 1900 mm, TE 5 2.52 mm, flip angle 5 908).

2.4

Task-based fMRI data analysis

All task-based fMRI data were preprocessed using SPM8 (http://www.

fil.ion.ucl.ac.uk/spm/software/spm8

/). As the magnetic field of the

scanner may be unstable during the initialization phase, the first ten

volumes for each subject were discarded (Dresler et al., 2017; Yao

et al., 2016). Therefore, only the remaining 170 volumes were used for

the subsequent analysis. The preprocessing of task-based fMRI data

included the following steps: slice timing (interlayer time correction),

realignment (head motion correction), co-registration (T1 images were

co-registered to the mean functional image), segmentation (T1 images

were divided into gray matter, white matter, and cerebrospinal fluid),

normalization (functional and structural imaging data were spatially nor-

malized to the Montreal Neurological Institute (MNI) EPI template

using DARTEL; Ashburner, 2007), and spatial smoothing (using a 3D

Gaussian kernel, full maximum at half width (FWHM) 5 6mm).Dueto

head movement issues (translational (mm) > 2.0 or rotation (8) > 2), six

task-based image datasets were discarded after preprocessing.

A general linear model (GLM) was applied to each voxel time

series, and each frequency condition was modelled with a separate

regressor constructed by convolving a boxcar function with a hemody-

namic response function provided by SPM8. Six motion parameters

were included in the model as nuisance regressor. One sample t tests

of beta maps were used to examine brain activation levels under each

frequency condition at the group level (p < .001, cluster size 5 10,

uncorrected). BrainNet Viewer (http://www.nitrc.org/projects/bnv)

(Xia, 2011) was used to map these activation maps to the same surface

map (Fig. 2). Points on the surface map that showed significant differ-

ences across frequencies (i.e., tonotopy) were labeled by assigning the

frequency x as the value of each given voxel if its beta value of fre-

quency x was the highest value. This method was similar to the method

performed by Humphries et al., 2010. The Brainnetome Atlas (Fan

et al., 2016) was used to fractionize the tonotopic areas in the HAC

(Supporting Information, Figure S1 and Table S1).

2.5

ROIs definition

The WFU PickAtlas (http://www.nitrc.org/projects/wfu_pickatlas/)

was used to produce ROIs. Each seed region is a sphere with a 5 mm

radius, and the center of each sphere is the peak point of each activa-

tion area. Multiple comparisons (p

FDR

<0.05, cluster 5 10) were per-

formed to select the peak points of each activation area used to

generate seed regions (Supporting Information, Table S2). Finally, the

WFU PickAtlas was used to combine the peak-point coordinates of

each frequency into a ROI. Six ROIs were defined based on the

responses of the 50 participants to the six pure tone frequencies, then

these ROIs were divided into three categories, that is, a high-frequency

(3200 and 6400 Hz), intermediate-frequency (800 and 1600 Hz), and

FIGURE 2 Tonotopic organization of the HAC. Heschl’sgyrusis

roughly surrounded by the white dotted line. Colored regions in

the bilateral hemispheres show voxels of s ignificantly different

frequencies, and the color of each voxel represents the frequency

that showed the highest activation response of the six frequency

tone stimu li [Color figure can be viewed at wileyonlinelibrary.com]

2226

YUAN ET AL.

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