人脑岛在情感处理中的不同功能分区研究

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"这篇文章是关于人脑岛叶在情绪感知中的功能研究，通过使用侵入式脑电图和电皮质刺激技术，探讨了在听觉情感识别中，人脑前部和后部岛叶的不同作用。研究发现，后部岛叶对听觉刺激有类似于赫氏沟的反应，而前部岛叶（AI）则像杏仁核那样对音频的情感内容作出反应。此外，从后部到AI，不同情感类型的区分度逐渐增强，揭示了岛叶内部从感觉向情感表示的转变过程。这项研究发表在《Cerebral Cortex》期刊，由多个大学的研究团队合作完成。" 文章详细阐述了大脑岛叶在情感感知和听觉处理中的功能细分。岛叶是大脑中一个复杂的结构，涉及到感官、运动和情感处理等多个方面。以往的研究虽然已经发现岛叶在这些过程中的活动，但对其内部细分区域的功能理解仍不充分。该研究的独特之处在于利用了癫痫患者植入的深度电极，通过直接在大脑内部记录和刺激来探究其功能。在听觉情绪感知实验中，研究人员将岛叶分为了后部和前部两个区域。后部岛叶的反应模式与负责初级听觉处理的赫氏沟相似，表明它在基础的听觉处理中起着关键作用。另一方面，前部岛叶，尤其是AI区域，对音频中的情感信息表现出强烈反应，这与杏仁核的反应模式相呼应，杏仁核在情绪识别中扮演着核心角色。此外，实验结果显示，从岛叶的后部到前部，对不同情感类型的区分度逐渐增加。这意味着情感处理的复杂性可能沿着岛叶的轴向从前到后递增，这为理解大脑如何将感官输入转化为情感体验提供了新的见解。这一发现对神经科学和心理学领域具有重要意义，有助于深入理解人类情绪认知的神经机制。同时，对于临床实践中涉及大脑岛叶的疾病，如癫痫、抑郁症等，也可能提供更精确的治疗策略。通过揭示大脑岛叶的精细功能分区，未来的研究可以进一步探索这些区域如何在更广泛的认知和情感处理中相互协作。

map of the single voxel in the insula as a distribution for calcu-

lating the Z-score. The Z-scores were then averaged across the

24 patients, cluster threshold k > 10.

iEEG Data

Stimuli and Task

Stimuli consisted of emotional and nonemotional sounds. The

nonemotional sounds included noise, 2 pure tones (PT), 2 har-

monic tones (Harm), Chinese speech /yi/ with 3 different pitch

contours (yi(2), yi(1), an d yi(4)), /da/, and phase-scrambled /da/.

The emotional sounds were taken from the Montreal Affec tive

Voices (

Belin et al. 2008), in which actors or actresses were

instructed to produce the vowel /a/ with different emotional

interjections. Voices from 2 identities (1 male and 1 female)

expressing 6 emotions (anger, fear, disgust, happy, sad, and

neutral) were chosen in this study. All stimuli were normalized

to the same sound level, were controlled by MATLAB (The

Mathworks, Natick, MA, USA) using the Psychophysics Toolbox

3.0 extension (

Brainard 1997), and were delivered via insert

air-conduction earphones. The volume was adjusted to a com-

fortable level of approximately 65 dB sound pressure level. All

stimuli were presented in a randomized order, and each was

repeated 20 times. Subjects needed to identify the emotion

type of the sound they heard by pressing a button after the

stimulus was over using the ha nd ipsilateral to the side of the

electrodes coverage.

iEEG Data Acquisition

iEEG signals were recorded via a 256-channel Neurofax EEG-

1200c ampliﬁer/digitizer system (Nihon Kohden, Japan) from

implanted depth electrodes with a high-pass ﬁlter of 0.01 Hz

cut-off frequency, a notch ﬁlter at 50 Hz, and a sampling rate of

2000 Hz. Electrodes placed on the inner surface of the skull

were used as the ground and reference.

Electrode Localization

We only focused on electrodes that had contacts in the insula,

HG, and the amygdala. The locations of the electrodes relative

to the cortical surface were determined using Freesurfer. We

reconstructed the individual brain’s 3D map with electrode

locations on the surface by aligning the presurgical high-

resolution, T1-weighted MRI obtained with a Philips Achieva

3.0 T TX scanner with the postsurgical computed tomography

(CT) images obtained using the Siemens SOMATOM Sensation

64 CT scanner. This registration was visually veriﬁed and man-

ually adjusted if necessary. To show all subjects’ implanted

electrodes on an average brain surface, we coregistered the

individual MRIs to the fsaverage brain through Freesurfer soft-

ware, and all electrodes were displayed on a 3D-constructed

cortical surface representing the average brain. Furthermore,

the electrode locations were also superimp osed onto the

inﬂated average brain for visualization. We analyzed electrodes

from the HG, the PI, the AI, and the amygdala separately. HG,

insula, and amygdala were de ﬁned and dissociated according

to the anatomic labels of the average brain in Freesurfer soft-

ware. The PI and the AI were deﬁned and dissociated accordin g

to the fMRI-clustering results.

iEEG Data Analysis

All analyses were performed in MATLAB. Each channel was

visually inspected for artifacts. Channels with epileptiform

activity were excluded from further analysis. We used notch

ﬁlters from Fieldtrip software (

http://www.ﬁeldtriptoolbox.org/)

to remove 50-Hz noise and its second and third harmonics, and

the data were down-sampled at 500 Hz. The data were then

segmented into a 200-ms prestimulus baseline and an 800-ms

poststimulus interval. All analyses focused on high-gamma

response (70–140 Hz). We used the term “following/broadband

response” in order to separate the phase-locking components

from the typical high-gamma (70–140 Hz) responses. The

Z-score of the high-gamma band was estimated using the fol-

lowing steps: 1) a 100-ms moving window (20-ms step) was

used to perform short-time Fourier transform for the prepro-

cessed iEEG signal, 2) each frequency component time series

was then normalized to its own baseline mean and was divided

by its own baseline standard deviation to determine its own Z-

score time series, 3) ﬁnally, Z-score time series of the “following

response” were averaged inside the range between F

and 10 Hz

and F

+ 10 Hz (F

: fundamental frequency of the sound stimu-

lus), whereas the average of the remaining frequency compo-

nents in the high-gamma band range produced the Z-score

time series of the broadband response. These normalization

procedures sought to cancel the 1/frequency decay of power in

the spectrum.

For each electrode, we deﬁned the broadband response

amplitude of each emotion type as the peak response of the

broadband response Z -score time series (200-ms prestimulus to

800-ms poststimulus). “Response amplitude” refers to the

mean broadband response amplitude of responses to the emo-

tional stimuli recording from an electrode, which reﬂects the

average response amplitude of single electrode to all emotional

stimuli. To quantify the emotion-selective capacity of single

electrodes, we deﬁned the emotion selectivity index (SI) as:

−

where r

−

is the mean broadband response amplitude to non-

emotional sounds, and

is the mean broadband response

amplitude to type i emotional sounds.

SI measured the response distance between the emotional

and nonemotional sounds. For visualization, the SI values were

all mapped onto the inﬂated average brain using MATLAB. We

conducted statistical analyses to compare the SI between dif-

ferent sites (HG, PI, AI, and amygdala) and different hemi-

spheres by 2-sample t-test with Bonferroni corrections.

We deﬁned a responsive trial if the Z-score of that trial

exceeded the 95% conﬁdence interval of the prestimulus base-

line mean and was maintained for at least 100 ms. The latency

was deﬁned as the peak latency of each responsive trial (see

Supplementary Materials).

To quantify the dissimilarity of neuronal responses elicited

by different types of emotion, we used Euclidean distance to

measure the pair-wise dissimilarities across electrodes in the

HG, the PI, the AI, and the amygdala and obtained the confu-

sion matrices. In these confusion matrices, larger dista nces

between the emotion pairs indicated higher dissimilarities of

their neural responses. We then deﬁned the normalized

response distance as the average distance across all emotion

pairs in HG, PI, AI, and the amygdala (see Supplementary

Materials).

ECS Data

In the clinical setting, the stimulation protocol sought to iden-

tify areas that caused typical seizure sensations and to map

Emotion and Auditory Processing by Human Insula Zhang et al.

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