We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.

We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.

A postdoc position is available in the lab of Geoff Goodhill at Washington University in St Louis for an NIH-funded project to help improve early diagnosis of ASD by using cutting-edge tools from machine learning and computational ethology. Motor differences are one of the earliest markers of increased ASD likelihood in infancy. Our goal is to develop broadly-applicable diagnostic tools which combine automated extraction of kinematic features from video data with new machine learning techniques to capture ASD motor function variability. As a first step, we are applying cutting-edge developments in computer vision, machine learning and computational ethology to a rich, longitudinal video dataset of infants being screened for ASD.

Individuals afflicted with certain types of autism spectrum disorder often exhibit impaired cognitive function alongside enhanced emotional symptoms and mood lability. However, current understanding of the pathogenesis of autism and intellectual disabilities is based primarily on studies in the hippocampus and cortex, brain areas involved in cognitive function. But, these disorders are also associated with strong emotional symptoms, which are likely to involve changes in the amygdala and other brain areas. In this talk I will highlight these issues by presenting analyses in rat models of ASD/ID lacking Nlgn3 and Frm1 (causing Fragile X Syndrome). In addition to identifying new circuit and cellular alterations underlying divergent patterns of fear expression, these findings also suggest novel therapeutic strategies.

Epilepsy is one of the most common neurological diseases according to the World Health Organization (WHO) affecting around 70 million people worldwide [WHO]. Patients who suffer from epilepsy also suffer from a variety of neuro-psychiatric co-morbidities, which they can experience as crippling as the seizure condition itself. Adequate organization of cerebral white matter is utterly important for cognitive development. The failure of integration of neurologic function with cognition is reflected in neuro-psychiatric disease, such as autism spectrum disorder (ASD). However, in epilepsy we know little about the importance of white matter abnormalities in epilepsy-associated co-morbidities. Epilepsy surgery is an important therapy strategy in patients where conventional anti-epileptic drug treatment fails . On histology of the resected brain samples, malformations of cortical development (MCD) are common among the epilepsy surgery population, especially focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC). Both pathologies are associated with constitutive activation of the mTOR pathway. Interestingly, some type of FCD is morphological similar to TSC cortical tubers including the abnormalities of the white matter. Hypomyelination with lack of myelin-producing cells, the oligodendrocytes, within the lesional area is a striking phenomenon. Impairment of the complex myelination process can have a major impact on brain function. In the worst case leading to distorted or interrupted neurotransmissions. It is still unclear whether the observed myelin pathology in epilepsy surgical specimens is primarily related to the underlying malformation process or is just a secondary phenomenon of recurrent epileptic seizures creating a toxic micro-environment which hampers myelin formation. Interestingly, mTORC1 has been implicated as key signal for myelination, thus, promoting the maturation of oligodendrocytes . These results, however, remain controversial. Regardless of the underlying pathophysiologic mechanism, alterations of myelin dynamics, depending on their severity, are known to be linked to various kinds of developmental disorders or neuropsychiatric manifestations.

In the lecture Yvette Roke and Jamie Hoefakker will discuss the positive and negative effects of daily stress on mental health. They will also highlight which characteristics are likely to cause more stress related issues, and why recovery time is very important. They will give an understanding of autism spectrum disorder (ASD) in relation to daily stress and they will discuss the app, SAM the stress autism mate, developed and investigated (SCED design) in co-creation with their patients with ASD.

Epilepsy is one of the most common neurological diseases according to the World Health Organization (WHO) affecting around 70 million people worldwide [WHO]. Patients who suffer from epilepsy also suffer from a variety of neuro-psychiatric co-morbidities, which they can experience as crippling as the seizure condition itself. Adequate organization of cerebral white matter is utterly important for cognitive development. The failure of integration of neurologic function with cognition is reflected in neuro-psychiatric disease, such as autism spectrum disorder (ASD). However, in epilepsy we know little about the importance of white matter abnormalities in epilepsy-associated co-morbidities. Epilepsy surgery is an important therapy strategy in patients where conventional anti-epileptic drug treatment fails . On histology of the resected brain samples, malformations of cortical development (MCD) are common among the epilepsy surgery population, especially focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC). Both pathologies are associated with constitutive activation of the mTOR pathway. Interestingly, some type of FCD is morphological similar to TSC cortical tubers including the abnormalities of the white matter. Hypomyelination with lack of myelin-producing cells, the oligodendrocytes, within the lesional area is a striking phenomenon. Impairment of the complex myelination process can have a major impact on brain function. In the worst case leading to distorted or interrupted neurotransmissions. It is still unclear whether the observed myelin pathology in epilepsy surgical specimens is primarily related to the underlying malformation process or is just a secondary phenomenon of recurrent epileptic seizures creating a toxic micro-environment which hampers myelin formation. Interestingly, mTORC1 has been implicated as key signal for myelination, thus, promoting the maturation of oligodendrocytes . These results, however, remain controversial. Regardless of the underlying pathophysiologic mechanism, alterations of myelin dynamics, depending on their severity, are known to be linked to various kinds of developmental disorders or neuropsychiatric manifestations.

In this talk, I will present the results of two recent systematic reviews and meta-analyses related to analogical reasoning and metaphor processing in autism, together with the results of a study that investigated verbal analogical reasoning and metaphor processing in the same sample of participants. Both metaphors and analogies rely on exploiting similarities, and they necessitate contextual processing. Nevertheless, our findings relating to metaphor processing and analogical reasoning showed distinct patterns. Whereas analogical reasoning emerged as a relative strength in autism, metaphor processing was found to be a relative weakness. Additionally, both meta-analytic studies investigated the relations between the level of intelligence of participants included in the studies, and the effect size of group differences between the autistic and typically developing (TD) samples. These analyses suggested in the case of analogical reasoning that the relative advantage of ASD participants might only be present in the case of individuals with lower levels of intelligence. By contrast, impairments in metaphor processing appeared to be more pronounced in the case of individuals with relatively lower levels of (verbal) intelligence. In our experimental study, we administered both verbal analogies and metaphors to the same sample of high-functioning autistic participants and TD controls. The two groups were matched on age, verbal IQ, working memory and educational background. Our aim was to understand better the similarities and differences between processing analogies and metaphors, and to see whether the advantage in analogical reasoning and disadvantage in metaphor processing is universal in autism.

Large genomic studies of individuals with autism spectrum disorders (ASD) have revealed approximately 100-200 high risk genes. However, whether these genes function in similar or different signaling networks in brain cells (neurons) remains poorly studied. We are using proteomic technology to build an ASD-associated signaling network map as a resource for the Autism research community. This resource can be used to study Autism risk genes and understand how pathways are convergent, and how patient mutations change the interaction profile. In this presentation, we will present how we developed a pipeline using neurons to build protein-protein interaction profiles. We detected previously unknown interactions between different ASD risk genes that have never been linked together before, and for some genes, we identified new signaling pathways that have not been previously reported. This resource will be available to the research community and will foster collaborations between ASD researchers to help accelerate therapeutics for ASD and related disorders.

Neocortical networks must generate and maintain stable activity patterns despite perturbations induced by learning and experience- dependent plasticity. There is abundant theoretical and experimental evidence that network stability is achieved through homeostatic plasticity mechanisms that adjust synaptic and neuronal properties to stabilize some measure of average activity, and this process has been extensively studied in primary visual cortex (V1), where chronic visual deprivation induces an initial drop in activity and ensemble average firing rates (FRs), but over time activity is restored to baseline despite continued deprivation. Here I discuss recent work from the lab in which we followed this FR homeostasis in individual V1 neurons in freely behaving animals during a prolonged visual deprivation/eye-reopening paradigm. We find that - when FRs are perturbed by manipulating sensory experience - over time they return precisely to a cell-autonomous set-point. Finally, we find that homeostatic plasticity is perturbed in a mouse model of Autism spectrum disorder, and this results in a breakdown of FRH within V1. These data suggest that loss of homeostatic plasticity is one primary cause of excitation/inhibition imbalances in ASD models. Together these studies illuminate the role of stabilizing plasticity mechanisms in the ability of neocortical circuits to recover robust function following challenges to their excitability.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting up to 1% of the population. Over the past few years, large-scale genomic studies have identified hundreds of genetic loci associated with liability to ASD. It is now time to translate these genetic discoveries into functional studies that can help us understand convergences and divergences across risk genes, and build pre-clinical cell and animal models. In this seminar, I will discuss some of the most recent findings on the genetic risk architecture of ASD. I will then expand on our work on biomarkers discovery and neurodevelopmental analyses in two rare genetic conditions associated with ASD: ADNP and DDX3X syndrome.

The thalamic reticular nucleus (TRN), the major source of thalamic inhibition, is known to regulate thalamocortical interactions critical for sensory processing, attention and cognition. TRN dysfunction has been linked to sensory abnormality, attention deficit and sleep disturbance across multiple neurodevelopmental disorders. Currently, little is known about the organizational principles underlying its divergent functions. In this talk, I will start with an example of how dysfunction of TRN contributes to attention deficit and sleep disruption using a mouse model of Ptchd1 mutation, which in humans cause neurodevelopmental disorder with ASD. Building on these findings, we further performed an integrative single-cell analysis linking molecular and electrophysiological features of the TRN to connectivity and systems-level function. We identified two subnetworks of the TRN with segregated anatomical structure, distinct electrophysiological properties, differential connections to the functionally distinct first-order and higher-order thalamic nuclei, and differential role in regulating sleep. These studies provide a comprehensive atlas for TRN neurons at the single-cell resolution and a foundation for studying diverse functions and dysfunctions of the TRN. Finally, I will describe the newly developed minimally invasive optogenetic tool for probing circuit function and dysfunction.