The Cerebellum and Autism: More than Motor Control

Autism spectrum disorder is a neurodevelopmental disorder characterized by deficits in social cognition at its core. Human and animal studies converge in the existence of a network of key brain structures involved in the perception, integration, and coding of social cues. These structures mainly involve areas traditionally associated with cognitive function, such as the prefrontal cortex; processing of emotions, such as the amygdala; and motivation and reward, such as the nucleus accumbens. The cerebellum, conventionally associated with motor functions, is lately being considered as a key structure within the social circuitry. Cerebellar neuroanatomical alterations are among the most replicated findings in postmortem brain samples of patients with autism. In addition, cerebellar defects have been proposed to affect the functioning of distal brain areas to which the cerebellum projects. In fact, animal studies support the inclusion of the cerebellum as part of the brain network regulating social cognition and provide a mechanistic tool to study its function within the social network. In this chapter, we review current evidence from human and animal studies, opening a new avenue for further research

Current Techniques for Investigating the Brain Extracellular Space

The brain extracellular space (ECS) is a continuous reticular compartment that lies between the cells of the brain. It is vast in extent relative to its resident cells, yet, at the same time the nano- to micrometer dimensions of its channels and reservoirs are commonly finer than the smallest cellular structures. Our conventional view of this compartment as largely static and of secondary importance for brain function is rapidly changing, and its active dynamic roles in signaling and metabolite clearance have come to the fore. It is further emerging that ECS microarchitecture is highly heterogeneous and dynamic and that ECS geometry and diffusional properties directly modulate local diffusional transport, down to the nanoscale around individual synapses. The ECS can therefore be considered an extremely complex and diverse compartment, where numerous physiological events are unfolding in parallel on spatial and temporal scales that span orders of magnitude, from milliseconds to hours, and from nanometers to centimeters. To further understand the physiological roles of the ECS and identify new ones, researchers can choose from a wide array of experimental techniques, which differ greatly in their applicability to a given sample and the type of data they produce. Here, we aim to provide a basic introduction to the available experimental techniques that have been applied to address the brain ECS, highlighting their main characteristics. We include current gold-standard techniques, as well as emerging cutting-edge modalities based on recent super-resolution microscopy. It is clear that each technique comes with unique strengths and limitations and that no single experimental method can unravel the unknown physiological roles of the brain ECS on its own.This work was supported by the grants from the Spanish Ministry for Research and Innovation SAF2017-83776-R and RYC-2014-15994 to JT, IJCI-2017-32114 to FS, University of the Basque Country grant GIU18/094 to OP and JT, and a Basque Government grant PIBA 2019-65 to JT

Semisupervised Speech Data Extraction from Basque Parliament Sessions and Validation on Fully Bilingual Basque–Spanish ASR

In this paper, a semisupervised speech data extraction method is presented and applied to create a new dataset designed for the development of fully bilingual Automatic Speech Recognition (ASR) systems for Basque and Spanish. The dataset is drawn from an extensive collection of Basque Parliament plenary sessions containing frequent code switchings. Since session minutes are not exact, only the most reliable speech segments are kept for training. To that end, we use phonetic similarity scores between nominal and recognized phone sequences. The process starts with baseline acoustic models trained on generic out-of-domain data, then iteratively updates the models with the extracted data and applies the updated models to refine the training dataset until the observed improvement between two iterations becomes small enough. A development dataset, involving five plenary sessions not used for training, has been manually audited for tuning and evaluation purposes. Cross-validation experiments (with 20 random partitions) have been carried out on the development dataset, using the baseline and the iteratively updated models. On average, Word Error Rate (WER) reduces from 16.57% (baseline) to 4.41% (first iteration) and further to 4.02% (second iteration), which corresponds to relative WER reductions of 73.4% and 8.8%, respectively. When considering only Basque segments, WER reduces on average from 16.57% (baseline) to 5.51% (first iteration) and further to 5.13% (second iteration), which corresponds to relative WER reductions of 66.7% and 6.9%, respectively. As a result of this work, a new bilingual Basque–Spanish resource has been produced based on Basque Parliament sessions, including 998 h of training data (audio segments + transcriptions), a development set (17 h long) designed for tuning and evaluation under a cross-validation scheme and a fully bilingual trigram language model.This work was partially funded by the Spanish Ministry of Science and Innovation (OPEN-SPEECH project, PID2019-106424RB-I00) and by the Basque Government under the general support program to research groups (IT-1704-22)

Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation

[Abstract] The huge amount of information stored in audio and video repositories makes search on speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given a spoken query. Research on this area is continuously fostered with the organization of QbE STD evaluations. This paper presents a multi-domain internationally open evaluation for QbE STD in Spanish. The evaluation aims at retrieving the speech files that contain the queries, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops; RTVE database, which includes broadcast television (TV) shows; and COREMAH database, which contains 2-people spontaneous speech conversations about different topics. The evaluation has been designed carefully so that several analyses of the main results can be carried out. We present the evaluation itself, the three databases, the evaluation metrics, the systems submitted to the evaluation, the results, and the detailed post-evaluation analyses based on some query properties (within-vocabulary/out-of-vocabulary queries, single-word/multi-word queries, and native/foreign queries). Fusion results of the primary systems submitted to the evaluation are also presented. Three different teams took part in the evaluation, and ten different systems were submitted. The results suggest that the QbE STD task is still in progress, and the performance of these systems is highly sensitive to changes in the data domain. Nevertheless, QbE STD strategies are able to outperform text-based STD in unseen data domains.Centro singular de investigación de Galicia; ED431G/04Universidad del País Vasco; GIU16/68Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-PMinisterio de Ciencia, Innovación y Competitividad; RTI2018-098091-B-I00Xunta de Galicia; ED431G/0

ALBAYZIN 2018 spoken term detection evaluation: a multi-domain international evaluation in Spanish

[Abstract] Search on speech (SoS) is a challenging area due to the huge amount of information stored in audio and video repositories. Spoken term detection (STD) is an SoS-related task aiming to retrieve data from a speech repository given a textual representation of a search term (which can include one or more words). This paper presents a multi-domain internationally open evaluation for STD in Spanish. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation task aims at retrieving the speech files that contain the terms, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the COREMAH database, which contains 2-people spontaneous speech conversations about different topics. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the results, and detailed post-evaluation analyses based on some term properties (within-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and native/foreign terms). Fusion results of the primary systems submitted to the evaluation are also presented. Three different research groups took part in the evaluation, and 11 different systems were submitted. The obtained results suggest that the STD task is still in progress and performance is highly sensitive to changes in the data domain.Ministerio de Economía y Competitividad; TIN2015-64282-R,Ministerio de Economía y Competitividad; RTI2018-093336-B-C22Ministerio de Economía y Competitividad; TEC2015-65345-PXunta de Galicia; ED431B 2016/035Xunta de Galicia; GPC ED431B 2019/003Xunta de Galicia; GRC 2014/024Xunta de Galicia; ED431G/01Xunta de Galicia; ED431G/04Agrupación estratéxica consolidada; GIU16/68Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-

An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies

Evaluation campaigns provide a common framework with which the progress of speech technologies can be effectively measured. The aim of this paper is to present a detailed overview of the IberSpeech-RTVE 2022 Challenges, which were organized as part of the IberSpeech 2022 conference under the ongoing series of Albayzin evaluation campaigns. In the 2022 edition, four challenges were launched: (1) speech-to-text transcription; (2) speaker diarization and identity assignment; (3) text and speech alignment; and (4) search on speech. Different databases that cover different domains (e.g., broadcast news, conference talks, parliament sessions) were released for those challenges. The submitted systems also cover a wide range of speech processing methods, which include hidden Markov model-based approaches, end-to-end neural network-based methods, hybrid approaches, etc. This paper describes the databases, the tasks and the performance metrics used in the four challenges. It also provides the most relevant features of the submitted systems and briefly presents and discusses the obtained results. Despite employing state-of-the-art technology, the relatively poor performance attained in some of the challenges reveals that there is still room for improvement. This encourages us to carry on with the Albayzin evaluation campaigns in the coming years.This work was partially supported by Radio Televisión Española through the RTVE Chair at the University of Zaragoza, and Red Temática en Tecnologías del Habla (RED2022-134270-T), funded by AEI (Ministerio de Ciencia e Innovación); It was also partially funded by the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie Grant 101007666; in part by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/ PRTR under Grants PDC2021-120846C41 PID2021-126061OB-C44, and in part by the Government of Aragon (Grant Group T3623R); it was also partially funded by the Spanish Ministry of Science and Innovation (OPEN-SPEECH project, PID2019-106424RB-I00) and by the Basque Government under the general support program to research groups (IT-1704-22), and by projects RTI2018-098091-B-I00 and PID2021-125943OB-I00 (Spanish Ministry of Science and Innovation and ERDF) as well

Multi-scale modeling of gene-behavior associations in an artificial neural network model of cognitive development

In the multi-disciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multi-scale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description, four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function versus structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene-behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake modeling multi-scale modeling with respect to cognition, and develop highly specific and complex hypotheses across multiple levels of description

Following the genes: a framework for animal modeling of psychiatric disorders

The number of individual cases of psychiatric disorders that can be ascribed to identified, rare, single mutations is increasing with great rapidity. Such mutations can be recapitulated in mice to generate animal models with direct etiological validity. Defining the underlying pathogenic mechanisms will require an experimental and theoretical framework to make the links from mutation to altered behavior in an animal or psychopathology in a human. Here, we discuss key elements of such a framework, including cell type-based phenotyping, developmental trajectories, linking circuit properties at micro and macro scales and definition of neurobiological phenotypes that are directly translatable to humans

Increased Sensory Processing Atypicalities in Parents of Multiplex ASD Families Versus Typically Developing and Simplex ASD Families

Recent studies have suggested that sensory processing atypicalities may share genetic influences with autism spectrum disorder (ASD). To further investigate this, the adolescent/adult sensory profile (AASP) questionnaire was distributed to 85 parents of typically developing children (P-TD), 121 parents from simplex ASD families (SPX), and 54 parents from multiplex ASD families (MPX). After controlling for gender and presence of mental disorders, results showed that MPX parents significantly differed from P-TD parents in all four subscales of the AASP. Differences between SPX and MPX parents reached significance in the Sensory Sensitivity subscale and also in subsequent modality-specific analyses in the auditory and visual domains. Our finding that parents with high genetic liability for ASD (i.e., MPX) had more sensory processing atypicalities than parents with low (i.e., SPX) or no (i.e., P-TD) ASD genetic liability suggests that sensory processing atypicalities may contribute to the genetic susceptibility for ASD