由於語言認知與複雜聲的關係，複雜聲的在人類皮層上的解碼吸引了學者長久以來的興趣。儘管如此，至今解碼的詳細機制仍不清楚，尤其是人腦皮層功能性分佈。現今聲音激發的電位已可以藉由置放於顱內的電極來測得，且是在人清醒的狀態下。我們有機會去分析由我們在美國的合作夥伴(美國愛荷華大學神經外科系)所收集的18名病人的腦電圖。我們問(a) 是否如果不同的聲音 (包含簡單、複雜、自然界的聲音)可以活化大腦顳葉不同的位置? (b) 是否這些特定區域上的反應差異是有用於區分不同的聲音? 為了回答這些問題，我們發展出一套特別的方法進行資料分析。首先，我們對原始資料進行實驗前處理，包含異常數值的移除、校正人工干擾的數值，接著用適應性過濾器(adaptive filter)去獲得訊雜比高的單次試驗的腦電圖。接著我們將(a)訊號的反應強度用平方平均數(RMS)呈現; (b) 背景的試驗間變異數用標準差呈現(SD)。我們觀察平方平均數與標準差的空間性分佈圖，發現是與聲音刺激種類有關。在無聲的情況下，強度與試驗間的變異數將呈現線性關係，這反射出底下的卜瓦松關係。在聲音刺激之下(包含合成的複雜聲，語言或者音樂)，平方平均數與標準差的關係會脫離卜瓦松關係，這反映出一些聲音引起的神經動態改變。典型地，有一個較大的區域接近於相對較小且有強大地平均平方數的反應區，且這較大的區域在標準標方面有降低的情形。這個變異數會下降的發現，與聲音刺激之後底下的神經元素進行同向反應或者被抑制一致。在Heschl聽覺皮層其中心部分由強大的平方平均數主導，而旁側的部分側帶區則由強大的標準反應所主導。在後外側顳PLST皮層，有強大標準反應的區域遠離有強大平方平均數反應，.似乎反應出這區域和副側帶區域的角色相似。我們在此總結，由這第一次發表的試驗間標準差是個有用的指標在於研究皮層的功能性分佈，同時也有用於區分複雜聲。實驗結果支持不同部分的大腦顳葉皮層(儘管有些部分重疊)包含於複雜聲的解碼。

Complex sounds coding in human has attracted long-standing interest due to its relationship with speech recognition. Despite of this, details of the underlying mechanisms remain poorly understood, especially regarding the fundamental question of functional localization in cortex. Sound-evoked potentials are now feasible to be recorded with intracranial electrocorticogram (ECoG) from awake humans of refractory epilepsy. We had the opportunity to get access to such ECoG signals from cortical grid- electrodes of 18 patients (collected in collaboration with the Department of Neurosurgery, Iowa University, USA). We asked: (a) if different sounds (simple, complex and naturally-occurring) would activate different parts of the temporal cortex, and (b) if so, what response metric of the ECoG could be useful for functional localization and hence for the machine-discrimination of sounds. To answer these questions, we developed a special approach of data analysis. First, we rejected response outliers and artifacts, before applying an adaptive filter to separate single trial evoked response from the background EEG. We then characterized (a) the evoked response in terms of its strength (or root-mean-square, RMS value) and (b) the background EEG in terms of its inter-trial variance (or more precisely standard deviation, SD). We observed characteristic spatial patterns of response RMS and SD that were sound-dependent. In silence, RMS and SD levels showed a linear relationship across space reflecting an underlying Poisson process. Upon episodic sound stimulation (both synthetic complex sounds, and speech or music), this RMS-SD relationship deviated from Poisson, reflecting the presence of some sound-induced neural dynamics. Typically, a larger area closed to (but not overlapping with) the more restricted area of strong RMS response, showed a characteristic drop in SD. This finding of a reduced SD or variation in the background EEG is consistent with the activity of the underlying neural elements being synchronized, if not suppressed, by sounds. At the auditory cortex (on Heschl’s gyrus), the medial part (auditory core) was dominated by a strong RMS response, whereas the lateral part (auditory belt) was dominated by a strong SD response. On the posterior lateral superior temporal (PLST) cortex, the regions of strong SD response consistently lied distal to the region of strong RMS response. This distal location, with respect to the core area, likely reflected their role as association processing areas (auditory parabelt). We conclude that the SD response, reported here for the first time, is a useful metric for studying functional localization of the cortex, and therefore potentially useful for the machine-discrimination of complex sounds. Results supported that different parts of the temporal cortex, despite of some functional overlap, are involved in the processing of complex acoustic features.

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