3,096 research outputs found

    Brain asymmetries related to language with emphasis on entorhinal cortex and basal forebrain

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    Anatomical asymmetries of the human brain are important in at least four respects: 1) they can serve as potential indicators of the evolutionary foundations of language, 2) they can be used for comparative analysis of neural specializations for communication in primates, 3) they may provide underlying structural correlates for functional imaging (fMRI, PET) and genetic studies, and finally 4) they can be used for studying disorders which are suspected to result from either disturbed development of cerebral asymmetry or asymmetric damage to the brain. In the first part of this review, we give a general framework of this field through the brief descriptions of the milestone discoveries and major conceptual advances as they emerged throughout the last 150 years. In the second part, we provide a more detailed view on the functional relevance that asymmetries of the entorhinal cortex and basal forebrain may have on the language

    Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies

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    Stories can have tremendous power -- not only useful for entertainment, they can activate our interests and mobilize our actions. The degree to which a story resonates with its audience may be in part reflected in the emotional journey it takes the audience upon. In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement. The system is applied to Hollywood films and high quality shorts found on the web. We begin by using deep convolutional neural networks for audio and visual sentiment analysis. These models are trained on both new and existing large-scale datasets, after which they can be used to compute separate audio and visual emotional arcs. We then crowdsource annotations for 30-second video clips extracted from highs and lows in the arcs in order to assess the micro-level precision of the system, with precision measured in terms of agreement in polarity between the system's predictions and annotators' ratings. These annotations are also used to combine the audio and visual predictions. Next, we look at macro-level characterizations of movies by investigating whether there exist `universal shapes' of emotional arcs. In particular, we develop a clustering approach to discover distinct classes of emotional arcs. Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives. These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement. Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other.Comment: Data Mining (ICDM), 2017 IEEE 17th International Conference o

    A Review on the Cognitive Neuroscience of Autism

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    With increased recognition in the media, heightened prevalence, and advances in research technologies, investigation into the causes of autism has broadened in recent years. Studies at the molecular, structural, and behavioral levels have resulted in significant findings, linking autism to qualitative differences in neurological function and an alteration of early development. Familial aggregation of autism demonstrate a strong genetic factor, although genetics can not completely account for its pathogenesis. Studies show autism having one of the most complex pathologies among neurodevelopmental disorders. Future studies applying sophisticated methodologies in new areas may shed light on current mysteries surrounding the disorder

    Behavioral sentiment analysis of depressive states

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    The need to release accurate and incontrovertible diagnoses of depression has fueled the search for new methodologies to obtain more reliable measurements than the commonly adopted questionnaires. In such a context, research has sought to identify non-biased measures derived from analyses of behavioral data such as voice and language. For this purpose, sentiment analysis techniques were developed, initially based on linguistic characteristics extracted from texts and gradually becoming more and more sophisticated by adding tools for the analyses of voice and visual data (such as facial expressions and movements). This work summarizes the behavioral features accounted for detecting depressive states and sentiment analysis tools developed to extract them from text, audio, and video recordings

    Etiological involvement of KCND1 variants in an X-linked neurodevelopmental disorder with variable expressivity

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    Utilizing trio whole-exome sequencing and a gene matching approach, we identified a cohort of 18 male individuals from 17 families with hemizygous variants in KCND1, including two de novo missense variants, three maternally inherited protein-truncating variants, and 12 maternally inherited missense variants. Affected subjects present with a neurodevelopmental disorder characterized by diverse neurological abnormalities, mostly delays in different developmental domains, but also distinct neuropsychiatric signs and epilepsy. Heterozygous carrier mothers are clinically unaffected. KCND1 encodes the ฮฑ-subunit of Kv4.1 voltage-gated potassium channels. All variant-associated amino acid substitutions affect either the cytoplasmic N- or C-terminus of the channel protein except for two occurring in transmembrane segments 1 and 4. Kv4.1 channels were functionally characterized in the absence and presence of auxiliary ฮฒ subunits. Variant-specific alterations of biophysical channel properties were diverse and varied in magnitude. Genetic data analysis in combination with our functional assessment shows that Kv4.1 channel dysfunction is involved in the pathogenesis of an X-linked neurodevelopmental disorder frequently associated with a variable neuropsychiatric clinical phenotype.</p

    Etiological involvement of KCND1 variants in an X-linked neurodevelopmental disorder with variable expressivity

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    Utilizing trio whole-exome sequencing and a gene matching approach, we identified a cohort of 18 male individuals from 17 families with hemizygous variants in KCND1, including two de novo missense variants, three maternally inherited protein-truncating variants, and 12 maternally inherited missense variants. Affected subjects present with a neurodevelopmental disorder characterized by diverse neurological abnormalities, mostly delays in different developmental domains, but also distinct neuropsychiatric signs and epilepsy. Heterozygous carrier mothers are clinically unaffected. KCND1 encodes the ฮฑ-subunit of Kv4.1 voltage-gated potassium channels. All variant-associated amino acid substitutions affect either the cytoplasmic N- or C-terminus of the channel protein except for two occurring in transmembrane segments 1 and 4. Kv4.1 channels were functionally characterized in the absence and presence of auxiliary ฮฒ subunits. Variant-specific alterations of biophysical channel properties were diverse and varied in magnitude. Genetic data analysis in combination with our functional assessment shows that Kv4.1 channel dysfunction is involved in the pathogenesis of an X-linked neurodevelopmental disorder frequently associated with a variable neuropsychiatric clinical phenotype.</p

    Music and the brain: disorders of musical listening

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    The study of the brain bases for normal musical listening has advanced greatly in the last 30 years. The evidence from basic and clinical neuroscience suggests that listening to music involves many cognitive components with distinct brain substrates. Using patient cases reported in the literature, we develop an approach for understanding disordered musical listening that is based on the systematic assessment of the perceptual and cognitive analysis of music and its emotional effect. This approach can be applied both to acquired and congenital deficits of musical listening, and to aberrant listening in patients with musical hallucinations. Both the bases for normal musical listening and the clinical assessment of disorders now have a solid grounding in systems neuroscience

    ์ฃผ์š” ์šฐ์šธ ์žฅ์• ์˜ ์Œ์„ฑ ๊ธฐ๋ฐ˜ ๋ถ„์„: ์—ฐ์†์ ์ธ ๋ฐœํ™”์˜ ์Œํ–ฅ์  ๋ณ€ํ™”๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2023. 2. ์ด๊ต๊ตฌ.Major depressive disorder (commonly referred to as depression) is a common disorder that affects 3.8% of the world's population. Depression stems from various causes, such as genetics, aging, social factors, and abnormalities in the neurotransmitter system; thus, early detection and monitoring are essential. The human voice is considered a representative biomarker for observing depression; accordingly, several studies have developed an automatic depression diagnosis system based on speech. However, constructing a speech corpus is a challenge, studies focus on adults under 60 years of age, and there are insufficient medical hypotheses based on the clinical findings of psychiatrists, limiting the evolution of the medical diagnostic tool. Moreover, the effect of taking antipsychotic drugs on speech characteristics during the treatment phase is overlooked. Thus, this thesis studies a speech-based automatic depression diagnosis system at the semantic level (sentence). First, to analyze depression among the elderly whose emotional changes do not adequately reflect speech characteristics, it developed the mood-induced sentence to build the elderly depression speech corpus and designed an automatic depression diagnosis system for the elderly. Second, it constructed an extrapyramidal symptom speech corpus to investigate the extrapyramidal symptoms, a typical side effect that can appear from an antipsychotic drug overdose. Accordingly, there is a strong correlation between the antipsychotic dose and speech characteristics. The study paved the way for a comprehensive examination of the automatic diagnosis system for depression.์ฃผ์š” ์šฐ์šธ ์žฅ์•  ์ฆ‰ ํ”ํžˆ ์šฐ์šธ์ฆ์ด๋ผ๊ณ  ์ผ์ปฌ์–ด์ง€๋Š” ๊ธฐ๋ถ„ ์žฅ์• ๋Š” ์ „ ์„ธ๊ณ„์ธ ์ค‘ 3.8%์— ๋‹ฌํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์ด ๊ฒช์€๋ฐ” ์žˆ๋Š” ๋งค์šฐ ํ”ํ•œ ์งˆ๋ณ‘์ด๋‹ค. ์œ ์ „, ๋…ธํ™”, ์‚ฌํšŒ์  ์š”์ธ, ์‹ ๊ฒฝ์ „๋‹ฌ๋ฌผ์งˆ ์ฒด๊ณ„์˜ ์ด์ƒ๋“ฑ ๋‹ค์–‘ํ•œ ์›์ธ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ์šฐ์šธ์ฆ์€ ์กฐ๊ธฐ ๋ฐœ๊ฒฌ ๋ฐ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ๊ด€๋ฆฌ๊ฐ€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ธ๊ฐ„์˜ ์Œ์„ฑ์€ ์šฐ์šธ์ฆ์„ ๊ด€์ฐฐํ•˜๊ธฐ์— ๋Œ€ํ‘œ์ ์ธ ๋ฐ”์ด์˜ค๋งˆ์ปค๋กœ ์—ฌ๊ฒจ์ ธ ์™”์œผ๋ฉฐ, ์Œ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœํ•œ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์Œ์„ฑ ๋ง๋ญ‰์น˜ ๊ตฌ์ถ•์˜ ์–ด๋ ค์›€๊ณผ 60์„ธ ์ดํ•˜์˜ ์„ฑ์ธ๋“ค์—๊ฒŒ ์ดˆ์ ์ด ๋งž์ถ”์–ด์ง„ ์—ฐ๊ตฌ, ์ •์‹ ๊ณผ ์˜์‚ฌ๋“ค์˜ ์ž„์ƒ ์†Œ๊ฒฌ์„ ๋ฐ”ํƒ•์œผ๋กœํ•œ ์˜ํ•™์  ๊ฐ€์„ค ์„ค์ •์˜ ๋ฏธํก๋“ฑ์˜ ํ•œ๊ณ„์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์˜๋ฃŒ ์ง„๋‹จ ๊ธฐ๊ตฌ๋กœ ๋ฐœ์ „ํ•˜๋Š”๋ฐ ํ•œ๊ณ„์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ํ•ญ์ •์‹ ์„ฑ ์•ฝ๋ฌผ์˜ ๋ณต์šฉ์ด ์Œ์„ฑ ํŠน์ง•์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ ๋˜ํ•œ ๊ฐ„๊ณผ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ„์˜ ํ•œ๊ณ„์ ๋“ค์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•œ ์˜๋ฏธ๋ก ์  ์ˆ˜์ค€ (๋ฌธ์žฅ ๋‹จ์œ„)์—์„œ์˜ ์Œ์„ฑ ๊ธฐ๋ฐ˜ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์šฐ์„ ์ ์œผ๋กœ ๊ฐ์ •์˜ ๋ณ€ํ™”๊ฐ€ ์Œ์„ฑ ํŠน์ง•์„ ์ž˜ ๋ฐ˜์˜๋˜์ง€ ์•Š๋Š” ๋…ธ์ธ์ธต์˜ ์šฐ์šธ์ฆ ๋ถ„์„์„ ์œ„ํ•ด ๊ฐ์ • ๋ฐœํ™” ๋ฌธ์žฅ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๋…ธ์ธ ์šฐ์šธ์ฆ ์Œ์„ฑ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ , ๋ฌธ์žฅ ๋‹จ์œ„์—์„œ์˜ ๊ด€์ฐฐ์„ ํ†ตํ•ด ๋…ธ์ธ ์šฐ์šธ์ฆ ๊ตฐ์—์„œ ๊ฐ์ • ๋ฌธ์žฅ ๋ฐœํ™”๊ฐ€ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ฐ์ • ์ „์ด๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๋…ธ์ธ์ธต์˜ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์˜ ๊ณผ๋ณต์šฉ์œผ๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ‘œ์ ์ธ ๋ถ€์ž‘์šฉ์ธ ์ถ”์ฒด์™ธ๋กœ ์ฆ์ƒ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์ถ”์ฒด์™ธ๋กœ ์ฆ์ƒ ์Œ์„ฑ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์˜ ๋ณต์šฉ๋Ÿ‰๊ณผ ์Œ์„ฑ ํŠน์ง•๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์šฐ์šธ์ฆ์˜ ์น˜๋ฃŒ ๊ณผ์ •์—์„œ ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์ด ์Œ์„ฑ์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด์„œ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ฃผ์š” ์šฐ์šธ ์žฅ์• ์˜ ์˜์—ญ์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Research Motivations 3 1.1.1 Bridging the Gap Between Clinical View and Engineering 3 1.1.2 Limitations of Conventional Depressed Speech Corpora 4 1.1.3 Lack of Studies on Depression Among the Elderly 4 1.1.4 Depression Analysis on Semantic Level 6 1.1.5 How Antipsychotic Drug Affects the Human Voice? 7 1.2 Thesis objectives 9 1.3 Outline of the thesis 10 Chapter 2 Theoretical Background 13 2.1 Clinical View of Major Depressive Disorder 13 2.1.1 Types of Depression 14 2.1.2 Major Causes of Depression 15 2.1.3 Symptoms of Depression 17 2.1.4 Diagnosis of Depression 17 2.2 Objective Diagnostic Markers of Depression 19 2.3 Speech in Mental Disorder 19 2.4 Speech Production and Depression 21 2.5 Automatic Depression Diagnostic System 23 2.5.1 Acoustic Feature Representation 24 2.5.2 Classification / Prediction 27 Chapter 3 Developing Sentences for New Depressed Speech Corpus 31 3.1 Introduction 31 3.2 Building Depressed Speech Corpus 32 3.2.1 Elements of Speech Corpus Production 32 3.2.2 Conventional Depressed Speech Corpora 35 3.2.3 Factors Affecting Depressed Speech Characteristics 39 3.3 Motivations 40 3.3.1 Limitations of Conventional Depressed Speech Corpora 40 3.3.2 Attitude of Subjects to Depression: Masked Depression 43 3.3.3 Emotions in Reading 45 3.3.4 Objectives of this Chapter 45 3.4 Proposed Methods 46 3.4.1 Selection of Words 46 3.4.2 Structure of Sentence 47 3.5 Results 49 3.5.1 Mood-Inducing Sentences (MIS) 49 3.5.2 Neutral Sentences for Extrapyramidal Symptom Analysis 49 3.6 Summary 51 Chapter 4 Screening Depression in The Elderly 52 4.1 Introduction 52 4.2 Korean Elderly Depressive Speech Corpus 55 4.2.1 Participants 55 4.2.2 Recording Procedure 57 4.2.3 Recording Specification 58 4.3 Proposed Methods 59 4.3.1 Voice-based Screening Algorithm for Depression 59 4.3.2 Extraction of Acoustic Features 59 4.3.3 Feature Selection System and Distance Computation 62 4.3.4 Classification and Statistical Analyses 63 4.4 Results 65 4.5 Discussion 69 4.6 Summary 74 Chapter 5 Correlation Analysis of Antipsychotic Dose and Speech Characteristics 75 5.1 Introduction 75 5.2 Korean Extrapyramidal Symptoms Speech Corpus 78 5.2.1 Participants 78 5.2.2 Recording Process 79 5.2.3 Extrapyramidal Symptoms Annotation and Equivalent Dose Calculations 80 5.3 Proposed Methods 81 5.3.1 Acoustic Feature Extraction 81 5.3.2 Speech Characteristics Analysis recording to Eq.dose 83 5.4 Results 83 5.5 Discussion 87 5.6 Summary 90 Chapter 6 Conclusions and Future Work 91 6.1 Conclusions 91 6.2 Future work 95 Bibliography 97 ์ดˆ ๋ก 121๋ฐ•
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