Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores

Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering issues like the excessive feature dimension, small sample size and missing data. Due to the limited data, a compact but expressive feature set is desirable as it can reduce the dimension and avoid the potential overfitting issue. Therefore, we pioneer the path signature method to further explore the essential hidden dynamic patterns of longitudinal cortical features. To form a hierarchical and more informative temporal representation, in this work, a novel cortical feature based path signature neural network (CF-PSNet) is proposed with stacked differentiable temporal path signature layers for prediction of individual cognitive scores. By introducing the existence embedding in path generation, we can improve the robustness against the missing data. Benefiting from the global temporal receptive field of CF-PSNet, characteristics consisted in the existing data can be fully leveraged. Further, as there is no need for the whole brain to work for a certain cognitive ability, a top K selection module is used to select the most influential brain regions, decreasing the model size and the risk of overfitting. Extensive experiments are conducted on an in-house longitudinal infant dataset within 9 time points. By comparing with several recent algorithms, we illustrate the state-of-the-art performance of our CF-PSNet (i.e., root mean square error of 0.027 with the time latency of 518 milliseconds for each sample)

Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review

Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 
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From Acoustic Segmentation to Language Processing: Evidence from Optical Imaging

During language acquisition in infancy and when learning a foreign language, the segmentation of the auditory stream into words and phrases is a complex process. Intuitively, learners use “anchors” to segment the acoustic speech stream into meaningful units like words and phrases. Regularities on a segmental (e.g., phonological) or suprasegmental (e.g., prosodic) level can provide such anchors. Regarding the neuronal processing of these two kinds of linguistic cues a left-hemispheric dominance for segmental and a right-hemispheric bias for suprasegmental information has been reported in adults. Though lateralization is common in a number of higher cognitive functions, its prominence in language may also be a key to understanding the rapid emergence of the language network in infants and the ease at which we master our language in adulthood. One question here is whether the hemispheric lateralization is driven by linguistic input per se or whether non-linguistic, especially acoustic factors, “guide” the lateralization process. Methodologically, functional magnetic resonance imaging provides unsurpassed anatomical detail for such an enquiry. However, instrumental noise, experimental constraints and interference with EEG assessment limit its applicability, pointedly in infants and also when investigating the link between auditory and linguistic processing. Optical methods have the potential to fill this gap. Here we review a number of recent studies using optical imaging to investigate hemispheric differences during segmentation and basic auditory feature analysis in language development

Being a beast machine: the somatic basis of selfhood

Modern psychology has long focused on the body as the basis of the self. Recently, predictive processing accounts of interoception (perception of the body ‘from within’) have become influential in accounting for experiences of body ownership and emotion. Here, we describe embodied selfhood in terms of ‘instrumental interoceptive inference’, which emphasises allostatic regulation and physiological integrity. We apply this approach to the distinctive phenomenology of embodied selfhood, accounting for its non-object-like character and subjective stability over time. Our perspective has implications for the development of selfhood, and illuminates longstanding debates about relations between life and mind, implying – contrary to Descartes – that experiences of embodied selfhood arise because of, and not in spite of, our nature as ‘beast machines’

Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Integrating reinforcement learning, equilibrium points and minimum variance to understand the development of reaching: a computational model

Despite the huge literature on reaching behaviour we still lack a clear idea about the motor control processes underlying its development in infants. This article contributes to overcome this gap by proposing a computational model based on three key hypotheses: (a) trial-anderror learning processes drive the progressive development of reaching; (b) the control of the movements based on equilibrium points allows the model to quickly find the initial approximate solution to the problem of gaining contact with the target objects; (c) the request of precision of the end-movement in the presence of muscular noise drives the progressive refinement of the reaching behaviour. The tests of the model, based on a two degrees of freedom simulated dynamical arm, show that it is capable of reproducing a large number of empirical findings, most deriving from longitudinal studies with children: the developmental trajectory of several dynamical and kinematic variables of reaching movements, the time evolution of submovements composing reaching, the progressive development of a bell-shaped speed profile, and the evolution of the management of redundant degrees of freedom. The model also produces testable predictions on several of these phenomena. Most of these empirical data have never been investigated by previous computational models and, more importantly, have never been accounted for by a unique model. In this respect, the analysis of the model functioning reveals that all these results are ultimately explained, sometimes in unexpected ways, by the same developmental trajectory emerging from the interplay of the three mentioned hypotheses: the model first quickly learns to perform coarse movements that assure a contact of the hand with the target (an achievement with great adaptive value), and then slowly refines the detailed control of the dynamical aspects of movement to increase accuracy

Child-Directed Speech and the Developing Brain: An Investigation of Adult Verbal Warmth and Negative Affect

This dissertation examines the association between the quality of children’s language experiences – as operationalized by adult verbal warmth – and their cognitive developmental outcomes. A socioeconomically diverse sample of 43 parents and their 5-to-9-year-old children participated in this study. A digital audio recording of the home environment was obtained, and children completed a high-resolution, structural MRI scan as well as direct assessments of their language and reading skills. The audio recordings were transcribed and coded using a coding scheme newly developed by the candidate in consultation with leading experts, in order to identify and quantify psycholinguistic elements of adult-child communication. Primary hypotheses included that adult verbal warmth is associated with (1) language and reading outcomes (2) the neural regions associated with each. To date, no studies have combined a transcription-based, fine-grained analysis of naturalistic home recordings with neuroimaging data. As such, this study represents a new line of inquiry at the nexus of developmental psychology, neuroscience, and education. The findings shed light on the impact of psychosocial language experiences on child development and on which forms of adult-child communication are most conducive to learning. Such information can inform programs that aim to teach parents ways to nurture their children’s development through high-quality child-directed speech. Social, educational, and clinical implications for mitigating risk factors and bolstering protective factors in order to, ultimately, foster healthy development for all children, are discussed

Ability of early neurological assessment and continuous EEG to predict long term neurodevelopmental outcome at 5 years in infants following hypoxic-ischaemic encephalopathy

Hypoxic-ischaemic encephalopathy (HIE) symptoms evolve during the first days of life and their monitoring is critical for treatment decisions and long-term outcome predictions. This thesis aims to report the five-year outcome of a HIE cohort born in the pre-therapeutic hypothermia era and to evaluate the predictive value of (a) neonatal neurological and EEG markers and (b) development in the first 24 months, for outcome. Methods: Participants were recruited at age five from two birth cohorts; HIE and Comparison. Repeated neonatal neurological assessments using the Amiel-TisonNeurological-Assessment-at-Term, continuous video EEG monitoring in the first 72 hours, and Sarnat grading at 24 hours were recorded. EEG severity grades were assigned at 6, 12 and 24 hours. Development was assessed in the HIE cohort at 6, 12 and 24 months using the Griffiths Mental Development (0-2) Revised Scales. At age five, intellectual (WPPSI-IIIUK scale), neuropsychological (NEPSY-II scales), neurological and ophthalmic testing was completed. Results: 5-year outcomes were available for 81.5% (n=53) of HIE and 71.4% (n=30) of Comparison cohorts. In HIE, 47.2% (27% mild, 47% moderate, 83% severe Sarnat), had non-intact outcome vs. 3.3% of the Comparison cohort. Non-intact outcome rates by 6-hour EEG-grade were: grade0=3%, grade1=25%, grade2=54%, grade3/4=79%. In HIE, processing speed (p=0.01) and verbal short-term memory (p=0.005) were below test norms. No significant differences were found in IQ, NEPSY-II or ocular biometry scores between children following mild and moderate HIE. Median IQ scores for mild (99(94-112),p=grade 2) at 24hours had superior positive predictive value (74%; AUROC(95%CI)=0.70(0.55-0.85) for non-intact 5-year outcome than abnormal EEG at 6 hours (68%; AUROC(95%CI)=0.71(0.56-0.87). Within-child development scores were inconsistent across the first 24 months. Although all children with intact 24-month Griffiths quotient (n=30) had intact 5-year IQ, 8/30 had non-intact overall outcome. Conclusion: Predictive value of neonatal neurological assessments and an EEG grading system for outcome was confirmed. Intact early childhood outcomes post-HIE may mask subtle adverse neuropsychological sequelae into the school years. This thesis supports emerging evidence that mild-grade HIE is not a benign condition and its inclusion in studies of neuroprotective treatments for HIE is warranted