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

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

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

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

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

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 α-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 β 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

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 α-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 β 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

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

주요 우울 장애의 음성 기반 분석: 연속적인 발화의 음향적 변화를 중심으로

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2023. 2. 이교구.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.주요 우울 장애 즉 흔히 우울증이라고 일컬어지는 기분 장애는 전 세계인 중 3.8%에 달하는 사람들이 겪은바 있는 매우 흔한 질병이다. 유전, 노화, 사회적 요인, 신경전달물질 체계의 이상등 다양한 원인으로 발생하는 우울증은 조기 발견 및 일상 생활에서의 관리가 매우 중요하다고 할 수 있다. 인간의 음성은 우울증을 관찰하기에 대표적인 바이오마커로 여겨져 왔으며, 음성 데이터를 기반으로한 자동 우울증 진단 시스템 개발을 위한 여러 연구들이 진행되어 왔다. 그러나 음성 말뭉치 구축의 어려움과 60세 이하의 성인들에게 초점이 맞추어진 연구, 정신과 의사들의 임상 소견을 바탕으로한 의학적 가설 설정의 미흡등의 한계점을 가지고 있으며, 이는 의료 진단 기구로 발전하는데 한계점이라고 할 수 있다. 또한, 항정신성 약물의 복용이 음성 특징에 미칠 수 있는 영향 또한 간과되고 있다. 본 논문에서는 위의 한계점들을 보완하기 위한 의미론적 수준 (문장 단위)에서의 음성 기반 자동 우울증 진단에 대한 연구를 시행하고자 한다. 우선적으로 감정의 변화가 음성 특징을 잘 반영되지 않는 노인층의 우울증 분석을 위해 감정 발화 문장을 개발하여 노인 우울증 음성 말뭉치를 구축하고, 문장 단위에서의 관찰을 통해 노인 우울증 군에서 감정 문장 발화가 미치는 영향과 감정 전이를 확인할 수 있었으며, 노인층의 자동 우울증 진단 시스템을 설계하였다. 최종적으로 항정신병 약물의 과복용으로 나타날 수 있는 대표적인 부작용인 추체외로 증상을 조사하기 위해 추체외로 증상 음성 말뭉치를 구축하였고, 항정신병 약물의 복용량과 음성 특징간의 상관관계를 분석하여 우울증의 치료 과정에서 항정신병 약물이 음성에 미칠 수 있는 영향에 대해서 조사하였다. 이를 통해 주요 우울 장애의 영역에 대한 포괄적인 연구를 진행하였다.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 초 록 121박