DSpace community: 生醫理工學院

絕對音感、自閉症特質與靜息態功能性連結關聯性之行為與腦造影研究;Relationship between absolute pitch, autistic traits and resting-state functional connectivity: Behavioral and neuroimaging studies

title: 絕對音感、自閉症特質與靜息態功能性連結關聯性之行為與腦造影研究;Relationship between absolute pitch, autistic traits and resting-state functional connectivity: Behavioral and neuroimaging studies abstract: 絕對音感（Absolute Pitch, AP）與自閉症光譜障礙（Autism Spectrum Disorder, ASD）常共同出現在特定個體中，兩者皆具有遺傳性與連續性，並與非典型的大腦連結模式相關。值得注意的是，在自閉症族群中，絕對音感能力的盛行率估計介於 5% 至 11% 之間。為解釋此絕對音感與自閉症特質之間的重疊現象，真實映射理論認為兩者可能共享某些特定的認知風格與神經連結特徵。然而，目前仍不清楚：（1）自閉症特質是否會影響絕對音感音樂家的表現；（2）這種關聯是否涉及自閉症三大腦網絡模型中的功能性連結異常。為釐清上述問題，我們進行了行為與靜息態功能性磁振造影實驗，以探討絕對音感能力與自閉症特質之間的關聯。行為實驗共招募了120 名受試者參與，依據絕對音感能力篩選準確度，將音樂家與非音樂家分為絕對音感組、非絕對音感組與非音樂家組，並比較其自閉症特質與音樂表現。所有受試者皆完成自閉症光譜量表，以評估自閉症特質的五個面向。此外，亦施測一系列與絕對音感能力相關的作業，包括音感調整測驗、相對音感辨識與音樂能力測驗。行為結果顯示，絕對音感組音樂家在「想像力」與「社交溝通」相關的自閉症特質分量表上得分顯著高於非絕對音感組與非音樂家。此外，在音樂能力測驗上絕對音感組音樂家音高辨識能力較佳，但在節奏辨識能力方面則無顯著關聯。我們運用靜息態功能性磁振造影技術探討絕對音感能力與自閉症特質在自閉症三大腦網絡模型中的功能性連結變化。招募了80名受試者參與，依據絕對音感能力篩選準確度，將音樂家與非音樂家分為絕對音感組、非絕對音感組與非音樂家組，並比較其自閉症特質與靜息態大腦功能之關聯性。結果顯示絕對音感組音樂家在預設模式網絡、警覺網絡與額頂葉網絡等自閉症相關核心腦區的功能性連結，與非絕對音感組及非音樂家組相比具有顯著差異。種子至體素與網絡內外分析結果進一步指出，絕對音感組音樂家呈現局部過度連結與整體低連結，且此連結模式與自閉症特質得分呈正相關。特別的是，在額頂葉網絡中，絕對音感組音樂家於中央前迴與額中迴呈現較高的活化程度，且此活化與自閉症特質及音樂節奏辨識能力呈正相關，可能反映兩者在此腦區的共同功能性參與。實驗結果顯示，來自行為與靜息態功能性磁振造影分析的結果提供了證據，支持絕對音感與自閉症特質之間的關聯性，並指出此關聯可能源自自閉症三大腦網絡模型中的功能性連結異常。儘管音樂訓練經驗與自閉症特質未呈現顯著相關，我們仍觀察到與自閉症特質及音樂能力相關的腦區活化。進一步地，我們排除行為共變數的影響，例如開始音樂訓練的年齡與音樂訓練累積時數。結果顯示，絕對音感音樂家的優勢仍然顯現在顳上迴、緣上迴、黑索氏迴、中央前迴、中央後迴、顳平面等腦區。此結果揭示，長期音樂訓練可能透過大腦可塑性，強化特定神經網絡的功能參與，並為理解音樂能力與自閉症特質的交互作用提供新的研究視角。 ;Absolute pitch (AP) and autism spectrum disorder (ASD) frequently co-occur in individuals. Both are heritable and continuous traits and are associated with atypical patterns of brain connectivity. Notably, the prevalence of AP in individuals with ASD has been estimated at 5% to 11%. To account for the overlap between absolute pitch and autistic traits, the veridical mapping theory proposes that the two may share specific cognitive styles and neural connectivity features. However, it remains unclear (1) whether autistic traits influence absolute pitch performance and (2) whether this relationship involves functional connectivity abnormalities within the three core large-scale networks implicated in autism. To address these questions, we conducted both behavioral and resting-state functional magnetic resonance imaging (rs-fMRI) experiments to investigate the association between absolute pitch ability and autistic traits. In the behavioral experiment, a total of 120 participants were recruited. Based on the accuracy of absolute pitch screening, categorized into AP, non-AP, and non-musician groups. All participants completed the Autism Spectrum Quotient (AQ), which evaluates autistic traits across five domains. In addition, a battery of AP-related tasks was administered, including the Pitch Adjustment Test (PAT), the Relative Pitch Test (RP), and the Advanced Measures of Music Audiation (AMMA). The behavioral results showed that AP musicians scored significantly higher than both non-AP musicians and non-musicians on the AQ subscales related to imagination and social communication. Furthermore, AP musicians outperformed others in tonal ability, while no significant effects were observed for rhythm ability. We employed resting-state functional magnetic resonance imaging (rs-fMRI) to examine alterations in functional connectivity between AP ability and autistic traits within the triple-network model of autism. We recruited 80 participants and found significant differences between AP musicians, non-AP musicians, and non-musicians in core autism-related networks, including the Default Mode Network (DMN), Salience Network (SN), and Fronto-Parietal Network (FPN). Seed-to-voxel and within–between network analyses further demonstrated that the AP group exhibited a pattern of local hyperconnectivity alongside global hypoconnectivity, which was positively correlated with autistic trait scores. Notably, within the FPN, AP musicians showed increased activation in the precentral gyrus (preCG) and middle frontal gyrus (MFG). This activation was positively correlated with both autistic traits and rhythm ability, which may reflect their shared functional involvement in this brain region. The observed behavioral and neural profiles suggest a meaningful convergence between AP and specific autistic traits, suggesting that this relationship may be driven by functional connectivity abnormalities within the triple-network model of autism. Despite musical training experience not significantly correlated with autistic traits, we observed neural activation in regions associated with both autistic traits and musical abilities. To further clarify these results, we controlled behavioral covariates such as age of onset of musical training and cumulative training hours. The findings revealed that AP musicians continued to exhibit enhanced activation in key auditory and sensorimotor regions. Overall, long-term musical training may enhance neuroplasticity and shed new light on the link between musical ability and autistic traits.

音頻拓樸對時間性音高知覺的影響;Tonotopic Effects on Temporal-Based Pitch Perception

title: 音頻拓樸對時間性音高知覺的影響;Tonotopic Effects on Temporal-Based Pitch Perception abstract: 音高知覺是語音理解、音樂欣賞以及聽覺場域分析中的關鍵因素。音高的產生大致來自兩個主要的機制理論：時間理論，源自神經元相位鎖定的放電模式以追蹤聲波的週期性；以及位置理論，源自耳蝸音頻拓樸(tonotopy)上的分佈。雖然兩種機制皆獲得支持，但它們的交互作用仍未被完全釐清，尤其在高頻音，調幅(AM)扮演更重要的角色。音訊中的調幅或包絡如何影響音高知覺，以及載波特性如何影響時間性音高 (time pitch)，仍是未解之謎。本研究使用轉置音(transposed tones)，將位置(載波)與時間(包絡)線索分離，以探討音頻拓樸對時間性音高的影響。實驗一，包絡被轉置到 1至10 kHz 的高頻載波以及噪音載波上。透過音高辨別、音程辨別與旋律識別任務，我們確認高頻聲音中的時間包絡能引發強而明顯的音高知覺，其表現隨著載波頻率增加而提升，且在純音載波上優於噪音載波。這些結果顯示，時間包絡對音高知覺的重要性，及其所受音頻拓樸的影響。實驗二進一步檢驗多載波的轉置音。雖然諧波會產生基音的音高知覺，但此現象在轉置後並不存在。我們發現，轉置音的音高知覺乃是由最低頻載波的包絡週期性所決定的。此外，我們更發現，包絡頻率比載波更能決定轉置音的音高知覺。為了分析這些結果，我們採用全域希爾伯特頻譜分析（Holo-Hilbert Spectral Analysis, HHSA），這是一種非線性方法，特別適合分析非線性與非穩態訊號，能提供載波頻率與調幅頻率的二維呈現。結果顯示，HHSA 呈現出的主要調幅頻率與轉置音的音高知覺完全相符。總結而言，本研究證明了時間性的資訊(包絡)能夠在高頻聲音中提供強而明顯的音高知覺。同時，HHSA 提供一個良好的聽覺訊號分析，可以分析出與音高知覺相符的調幅頻率。這些基於轉置音實驗與 HHSA 的研究，不僅深化了在音高知覺中，音頻拓樸與時間訊息交互作用的理解，也為聽覺輔具的發展提出了新的方向。 ;Pitch perception is essential to speech understanding, music appreciation, and auditory scene analysis. It arises from two complementary mechanisms: the time theory, derived from phase-locked neural firing patterns tracking waveform periodicity, and the place theory, derived from excitation along the cochlear tonotopic map. While both mechanisms are supported, their interaction remains unresolved, particularly in high-frequency hearing where amplitude modulation (AM) cues dominate. Although AM is preserved throughout the auditory system, how it contributes to pitch perception and how carrier properties shape temporal pitch remain open questions. This dissertation uses transposed tones, which dissociate spectral (carrier/place) and temporal (envelope/time) cues, to probe tonotopic influences on temporal-based pitch. In Experiment 1, AM envelopes were transposed onto carriers from 1 to 10 kHz and onto noise carriers. Pitch discrimination, interval discrimination, and melody identification tasks confirmed that temporal envelope fluctuations in high-frequency sounds evoke a robust pitch percept. Performance improved with increasing carrier frequency and was stronger for tonal than noise carriers. These findings indicate that pitch information provided by temporal envelope is more pronounced than previously assumed and shaped by tonotopic position. Experiment 2 extended this by examining transposed tones on multiple carriers. Though harmonic complexes normally produce a fundamental pitch, this phenomenon failed to preserved after transposition. We found the envelope periodicity on the lowest-frequency tonotopy dominates the pitch of transposed tones. Furthermore, we found pitch perception is driven more by envelope frequency than carrier spectrum in transposed tones. To analyze these results, we employed Holo-Hilbert Spectral Analysis (HHSA), a nonlinear method providing a two-dimensional representation of instantaneous frequency and AM. Unlike Fourier or wavelet analyses, HHSA is adaptive and suitable for nonlinear and non-stationary signals such as speech or music. HHSA consistently revealed the dominant AM frequency that matched perceived pitch. In summary, this work demonstrates that temporal envelope cues can support robust pitch perception at high frequencies. HHSA further provides a powerful analytic framework to reveal the AM dynamics underlying these percepts. These findings from transposed-tone experiments and HHSA advance understanding of the interplay between spectral and temporal coding in pitch perception and suggest new directions for auditory prosthetics.

成人與青少年皮肌炎轉錄體途徑的生物資訊分析及潛在藥物預測;Bioinformatic Analysis of Transcriptomic Pathways in Adult and Juvenile Dermatomyositis with Potential Drug Prediction

title: 成人與青少年皮肌炎轉錄體途徑的生物資訊分析及潛在藥物預測;Bioinformatic Analysis of Transcriptomic Pathways in Adult and Juvenile Dermatomyositis with Potential Drug Prediction abstract: 皮肌炎（Dermatomyositis, DM）與幼年型皮肌炎（Juvenile Dermatomyositis, JDM）為一類罕見的特發性發炎性肌肉病變（Idiopathic Inflammatory Myopathies, IIMs），其特徵為骨骼肌慢性發炎與免疫反應導致的組織損傷。雖然成人與幼年型皮肌炎在臨床表現及病理組織學上有許多相似之處，但近年研究顯示兩者在病生理機轉上也存在差異。本研究目的為使用生物資訊學方法，系統性分析成人與幼年型皮肌炎的共同與特異性轉錄體特徵，並利用藥物篩選平台(drug screening platform)預測潛在的治療候選化合物。本研究自 Gene Expression Omnibus (GEO) 資料庫取得肌肉切片微陣列資料集，包括成人皮肌炎 GSE128470、GSE5370、GSE2044 及幼年型皮肌炎 GSE11971、GSE3307。利用 TAC軟體進行批次校正與差異表達基因分析（DEG analysis）。經由 Gene Ontology (GO) 與 Kyoto Encyclopedia of Genes and Genomes (KEGG) 進行功能富集分析，並透過 STRING 與 Cytoscape 建立蛋白質交互作用網路（PPI network）及篩選樞紐基因（hub genes）。候選藥物則以 LINCS L1000CDS2 平台進行預測。結果顯示，成人皮肌炎共鑑定出 850 個差異表達基因，主要富集於抗病毒、發炎與免疫相關路徑，包括第一型干擾素反應、MAPK 及 NOD-like receptor 訊號途徑。幼年型皮肌炎共鑑定出 2,544 個差異表達基因，顯示出干擾素驅動的先天免疫活化、細胞黏附、血管新生及 PI3K–Akt 訊號上調。兩組比較後共有 437 個重疊基因，顯示兩者皆具有干擾素介導之「類病毒免疫表現型」；然而，幼年型皮肌炎呈現更明顯的血管重塑與細胞黏附。樞紐基因分析指出 STAT1, DDX58, IFIH1, CXCL10, ISG15, MX1, GBP1, EIF2AK2 為疾病關鍵調控基因。藥物預測結果中，XMD-1150 與 KM 00927 同時被預測為成人與幼年型皮肌炎的共同候選治療化合物。綜合而言，本研究以整合轉錄體分析闡明了成人與幼年型皮肌炎之共同與差異的分子機制，指出干擾素介導的免疫活化為兩者共同核心特徵，而血管與細胞黏附訊號的上調則以幼年型皮肌炎較為顯著。所預測之候選藥物提供了後續實驗驗證與精準治療開發的潛在方向，對於難治型皮肌炎具有重要臨床應用價值。 ;Dermatomyositis (DM) and juvenile dermatomyositis (JDM) are rare idiopathic inflammatory myopathies characterized by chronic muscle inflammation and immune-mediated tissue injury. Despite overlapping clinical and histopathological features, increasing evidence suggests that adult and juvenile DM also possess distinct features of pathogenesis. This study aimed to systematically elucidate the shared and unique transcriptomic landscapes of DM and JDM and to predict potential therapeutic candidates through integrative bioinformatics and drug repurposing approaches. Publicly available microarray datasets of muscle biopsy specimens (GSE128470, GSE5370, and GSE2044 for DM; GSE11971 and GSE3307 for JDM) were obtained from the Gene Expression Omnibus (GEO) database. Batch correction and differential expression analyses were conducted using the Transcriptome Analysis Console (TAC) software. Functional enrichment analyses based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify key biological pathways, while protein–protein interaction (PPI) networks and hub gene analyses were conducted using STRING and Cytoscape. Candidate compounds were predicted via the LINCS L1000CDS2 platform. In DM, 850 DEGs were identified, predominantly enriched in antiviral, inflammatory, and immune signaling pathways, including type I interferon response, MAPK, and NOD-like receptor signaling. In JDM, 2,544 DEGs were identified, highlighting interferon-driven innate immunity, cell adhesion, angiogenesis, and PI3K–Akt signaling. Comparative analyses revealed 437 overlapping DEGs, underscoring a shared interferon-mediated viral-like immune signature across age groups, while JDM exhibited enhanced vascular remodeling and adhesion-related processes. Hub gene analysis identified STAT1, DDX58, IFIH1, CXCL10, ISG15, MX1, GBP1, EIF2AK2 as central regulators. Drug repurposing predicted XMD-1150 and KM 00927 as potential shared therapeutic candidates for DM and JDM. In conclusion, this integrative transcriptomic study delineates both shared and distinct molecular pathways in DM and JDM, highlighting interferon-driven immune activation as a common hallmark and vascular-adhesion signaling as a distinguishing feature in JDM. The predicted compounds provide a foundation for future experimental validation and development of targeted therapies in refractory dermatomyositis.

使用人工智慧在不使用參考物體下測量大腸息肉大小;Using artificial intelligence measuring colon polyp size without a reference object

title: 使用人工智慧在不使用參考物體下測量大腸息肉大小;Using artificial intelligence measuring colon polyp size without a reference object abstract: 背景息肉尺寸為決定大腸鏡追蹤間隔之關鍵因素之一。本研究提出一種無需參考物即可於大腸鏡檢查過程中量測息肉尺寸大小的人工智慧模型。材料與方法本研究建立之息肉尺寸估測迴歸模型，係以兩個獨立之 SegFormer 模型輸出為基礎，其中一者用於息肉分割；另一者用於深度估測。模型初期以仿體在模擬大腸模型進行訓練，隨後透過 1,304 張臨床影像進行遷移式學習。測試階段使用獨立於訓練集之外的52個息肉，共178張影像。以套圈法作為尺寸比較之真實值，採用Olympus 290系列大腸鏡（視野角170°）進行拍攝。息肉依尺寸分為三組：≦5mm、5–10mm 與 ≧10mm。統計分析包括誤差率分析、召回率、精確度、Bland-Altman分析圖、配對 t 檢定及 Cohen′s kappa，以評估套圈法與AI 模型之間在量化測量與分類一致性上的表現。結果於測試模型中，AI 模型在三種息肉尺寸組別之誤差率分別為 10.74%、12.36% 與 9.89%，平均誤差率為 11.47%。整體召回率為 0.846；三組息肉尺寸的精確度分別為 0.870、0.911 與 0.857，平均精確度為 0.879；整體 F1 分數為 0.861。Cohen′s kappa 值為 0.792，顯示兩種方法間具高度一致性。Bland-Altman 分析顯示兩方法間之平均偏差為 -0.03 mm，一致性界限介於 -1.654mm 至 1.596mm。結論本研究所建立之人工智慧模型可於結腸鏡檢查中，在無需參考物的情況下，準確量測大腸息肉尺寸，具臨床應用潛力與發展前景。 ;Background Polyp size is one of the key factors in determining colonoscopy surveillance intervals. We present an artificial intelligence model for colon polyp size measurement that does not require a reference object during a colonoscopy. Materials and Methods The regression model for polyp size estimation was developed using the outputs from two independent SegFormer models, one for polyp segmentation and the other for depth estimation. Initially, colonoscopic images of polyp phantoms were used to build the model, followed by transfer learning on 1,304 real-world images. For model testing, 178 polyp images from 52 polyps, independent of the training set, were evaluated. A snare was used as the ground truth for size comparison with the AI-based model. Olympus 290 series colonoscope with field of view (FOV) of 170 angle was used in this study. Polyps were categorized into three size groups: ≦5 mm, 5–10 mm, and ≧10 mm. Statistical analysis include error rate analysis, recall, precision, Bland–Altman plot, paired t-test, and Cohen′s kappa to evaluate both the quantitative agreement and categorical consistency between the snare method and AI-based model. Results The error rates for the snare method and the AI-based model across the three polyp size groups in testing model were 10.74%, 12.36%, and 9.89%, respectively, with an average error rate of 11.47%. The overall recall was 0.846, and precision rates for the three size groups were 0.870, 0.911, and 0.857, resulting in an average precision of 0.879. The overall F1 score was 0.861. Cohen′s kappa value between the two methods across the three groups was 0.792. Bland-Altman analysis showed a mean bias of -0.03 mm between the two methods, with limits of agreement, from -1.654 mm to 1.596 mm. Conclusion Our AI-based model shows promise as an accurate tool for colorectal polyp size measurement without the need for a reference object during screening colonoscopy.