Dysfunctions in sensorimotor control and decision processing in schizophrenia

Στην παρούσα εργασία μελετήθηκε η βιβλιογραφία η σχετιζόμενη με την δυσλειτουργία του κινητικοαισθητικού ελέγχου και της επεξεργασίας λήψης αποφάσεων στην σχιζοφρένεια. Αρχικά, στην εισαγωγή γίνεται μια σύντομη περιγραφή των πιο χαρακτηριστικών συμπτωμάτων της διαταραχής. Ακολουθεί η ανατομική παρουσίαση των εγκεφαλικών δικτύων που μπορεί να παρουσιάζουν βλάβη στην σχιζοφρένεια. Κατόπιν εξετάζεται ο τρόπος επεξεργασίας της λήψης αποφάσεων σε υγιείς και σχιζοφρενείς. Το επόμενο κεφάλαιο παρουσιάζει την βιβλιογραφία σχετικά με τις κινητικοαισθητικές ανωμαλίες που απαντώνται στην σχιζοφρένεια. Έγινε προσπάθεια να συνδεθούν οι τύποι των κινητικοαισθητικών ανωμαλιών με τις ανατομικές τους βλάβες. Επίσης ελήφθησαν υπ’ όψη τα νευροφυσιολογικά δεδομένα και οι απεικονιστικές μελέτες. Κατόπιν παρουσιάζονται οι μελέτες που έγιναν με την χρήση χειρονακτικών και σακκαδικών χρόνων αντίδρασης που και οι δύο είναι γνωστό ότι επηρεάζονται στην σχιζοφρένεια. Επίσης παρουσιάζονται μελέτες που χρησιμοποίησαν ταυτόχρονα χειρονακτικούς και σακκαδικούς χρόνους αντίδρασης. Στο τέλος της μελέτης υπάρχει μια συζήτηση που αξιολογεί περιληπτικά τα ευρήματα της τεράστιας βιβλιογραφίας εν μέσω των ετών μαζί με υποδείξεις για περαιτέρω τρόπους διερεύνησης των δυσλειτουργιών του κινητικοαισθητικού ελέγχου και της λήψης αποφάσεων στην σχιζοφρένεια.In the present study, the literature on the dysfunctions of the sensorimotor control and the processes of decision making in schizophrenia (SZ) is reviewed. At the beginning, there is an introduction with a brief description of the most characteristic symptoms of the disorder. The anatomic presentation of the brain networks that may be lesioned in SZ follows. Afterwards, the way decision making is taking place in health and in SZ is presented. The next chapter presents a literature review on the sensorimotor abnormalities which are encountered in SZ. Effort was taken to combine the types of sensorimotor abnormalities with their anatomic lesions. Neurophysiological data and imaging studies were also taken into consideration. Next, are presented the studies which have been performed using manual and saccadic reaction times, which are both known to be affected in SZ. Studies that have used manual reaction time and saccadic reaction times simultaneously are also presented. At the end of the study there is a discussion summarizing the findings of the vast literature through the years together with suggestions for further ways of exploring the dysfunctions of the sensorimotor control and the decision making in SZ

Methods to assess changes in human brain structure across the lifecourse

Human brain structure can be measured across the lifecourse (“in vivo”) with magnetic resonance imaging (MRI). MRI data are often used to create “atlases” and statistical models of brain structure across the lifecourse. These methods may define how brain structure changes through life and support diagnoses of increasingly common, yet still fatal, age-related neurodegenerative diseases. As diseases such as Alzheimer’s (AD) cast an ever growing shadow over our ageing population, it is vitally important to robustly define changes which are normal for age and those which are pathological. This work therefore assessed existing MR brain image data, atlases, and statistical models. These assessments led me to propose novel methods for accurately defining the distributions and boundaries of normal ageing and pathological brain structure. A systematic review found that there were fewer than 100 appropriately tested normal subjects aged ≥60 years openly available worldwide. These subjects did not have the range of MRI sequences required to effectively characterise the features of brain ageing. The majority of brain image atlases identified in this review were found to contain data from few or no subjects aged ≥60 years and were in a limited range of MRI sequences. All of these atlases were created with parametric (mean-based) statistics that require the assumptions of equal variance and Gaussian distributions. When these assumptions are not met, mean-based atlases and models may not well represent the distributions and boundaries of brain structure. I tested these assumptions and found that they were not met in whole brain, subregional, and voxel-based models of ~580 subjects from across the lifecourse (0- 90 years). I then implemented novel whole brain, subregional, and voxel-based statistics, e.g. percentile rank atlases and nonparametric effect size estimates. The equivalent parametric statistics led to errors in classification and inflated effects by up to 45% in normal ageing-AD comparisons. I conclude that more MR brain image data, age appropriate atlases, and nonparametric statistical models are needed to define the true limits of normal brain structure. Accurate definition of these limits will ultimately improve diagnoses, treatment, and outcome of neurodegenerative disease

The role of phonology in visual word recognition: evidence from Chinese

Posters - Letter/Word Processing V: abstract no. 5024The hypothesis of bidirectional coupling of orthography and phonology predicts that phonology plays a role in visual word recognition, as observed in the effects of feedforward and feedback spelling to sound consistency on lexical decision. However, because orthography and phonology are closely related in alphabetic languages (homophones in alphabetic languages are usually orthographically similar), it is difficult to exclude an influence of orthography on phonological effects in visual word recognition. Chinese languages contain many written homophones that are orthographically dissimilar, allowing a test of the claim that phonological effects can be independent of orthographic similarity. We report a study of visual word recognition in Chinese based on a mega-analysis of lexical decision performance with 500 characters. The results from multiple regression analyses, after controlling for orthographic frequency, stroke number, and radical frequency, showed main effects of feedforward and feedback consistency, as well as interactions between these variables and phonological frequency and number of homophones. Implications of these results for resonance models of visual word recognition are discussed.postprin

Interactive effects of orthography and semantics in Chinese picture naming

Posters - Language Production/Writing: abstract no. 4035Picture-naming performance in English and Dutch is enhanced by presentation of a word that is similar in form to the picture name. However, it is unclear whether facilitation has an orthographic or a phonological locus. We investigated the loci of the facilitation effect in Cantonese Chinese speakers by manipulating—at three SOAs (2100, 0, and 1100 msec)—semantic, orthographic, and phonological similarity. We identified an effect of orthographic facilitation that was independent of and larger than phonological facilitation across all SOAs. Semantic interference was also found at SOAs of 2100 and 0 msec. Critically, an interaction of semantics and orthography was observed at an SOA of 1100 msec. This interaction suggests that independent effects of orthographic facilitation on picture naming are located either at the level of semantic processing or at the lemma level and are not due to the activation of picture name segments at the level of phonological retrieval.postprin

Hallucinated and spoken linguistic patterns as markers of psychiatric disorders

A person with alterations in the brain and cognitive functioning and whose language- and speech-related processes are affected might experience atypical linguistic phenomena. Hallucinated voices, also known as auditory verbal hallucinations, illustrate this: when they manifest, an individual can hear words, phrases or dialogues that can resemble actual human language production in the absence of an actual source of the voice in the outer world. Disorganized speech represents another example: an individual might express her/himself with a discourse whose associations of concepts and use of grammatical elements area typical, sometimes until the point in which the speech is not understandable anymore. Since these linguistic phenomena occur both in individuals with and without need for mental care, a main clinical problem consist in accurately and consistently identifying the patterns that differentiate between pathological and non-pathological hallucinated voices or disorganized speech. In this thesis, a combination of linguistic theory, computational methods, and artificial intelligence was implemented to unveil the linguistic patterns that distinguish between pathological and non-pathological hallucinated voices, as well as those that differentiate the speech of patients with schizophrenia-spectrum disorders from that of control individuals. More broadly, a consensual framework was developed regarding the potential use of this approach across psychiatric disorders and for a series of clinical actions. Lastly, pending obstacles and emerging questions related to this approach were underlined, followed by tentative theoretical venues that might point into solutions and answers about “Hallucinated and spoken linguistic patterns as markers of psychiatric disorders”

Automatic Detection of Dementia and related Affective Disorders through Processing of Speech and Language

In 2019, dementia is has become a trillion dollar disorder. Alzheimer’s disease (AD) is a type of dementia in which the main observable symptom is a decline in cognitive functions, notably memory, as well as language and problem-solving. Experts agree that early detection is crucial to effectively develop and apply interventions and treatments, underlining the need for effective and pervasive assessment and screening tools. The goal of this thesis is to explores how computational techniques can be used to process speech and language samples produced by patients suffering from dementia or related affective disorders, to the end of automatically detecting them in large populations us- ing machine learning models. A strong focus is laid on the detection of early stage dementia (MCI), as most clinical trials today focus on intervention at this level. To this end, novel automatic and semi-automatic analysis schemes for a speech-based cogni- tive task, i.e., verbal fluency, are explored and evaluated to be an appropriate screening task. Due to a lack of available patient data in most languages, world-first multilingual approaches to detecting dementia are introduced in this thesis. Results are encouraging and clear benefits on a small French dataset become visible. Lastly, the task of detecting these people with dementia who also suffer from an affective disorder called apathy is explored. Since they are more likely to convert into later stage of dementia faster, it is crucial to identify them. These are the fist experiments that consider this task us- ing solely speech and language as inputs. Results are again encouraging, both using only speech or language data elicited using emotional questions. Overall, strong results encourage further research in establishing speech-based biomarkers for early detection and monitoring of these disorders to better patients’ lives.Im Jahr 2019 ist Demenz zu einer Billionen-Dollar-Krankheit geworden. Die Alzheimer- Krankheit (AD) ist eine Form der Demenz, bei der das Hauptsymptom eine Abnahme der kognitiven Funktionen ist, insbesondere des Gedächtnisses sowie der Sprache und des Problemlösungsvermögens. Experten sind sich einig, dass eine frühzeitige Erkennung entscheidend für die effektive Entwicklung und Anwendung von Interventionen und Behandlungen ist, was den Bedarf an effektiven und durchgängigen Bewertungsund Screening-Tools unterstreicht. Das Ziel dieser Arbeit ist es zu erforschen, wie computergest ützte Techniken eingesetzt werden können, um Sprach- und Sprechproben von Patienten, die an Demenz oder verwandten affektiven Störungen leiden, zu verarbeiten, mit dem Ziel, diese in großen Populationen mit Hilfe von maschinellen Lernmodellen automatisch zu erkennen. Ein starker Fokus liegt auf der Erkennung von Demenz im Frühstadium (MCI), da sich die meisten klinischen Studien heute auf eine Intervention auf dieser Ebene konzentrieren. Zu diesem Zweck werden neuartige automatische und halbautomatische Analyseschemata für eine sprachbasierte kognitive Aufgabe, d.h. die verbale Geläufigkeit, erforscht und als geeignete Screening-Aufgabe bewertet. Aufgrund des Mangels an verfügbaren Patientendaten in den meisten Sprachen werden in dieser Arbeit weltweit erstmalig mehrsprachige Ansätze zur Erkennung von Demenz vorgestellt. Die Ergebnisse sind ermutigend und es werden deutliche Vorteile an einem kleinen französischen Datensatz sichtbar. Schließlich wird die Aufgabe untersucht, jene Menschen mit Demenz zu erkennen, die auch an einer affektiven Störung namens Apathie leiden. Da sie mit größerer Wahrscheinlichkeit schneller in ein späteres Stadium der Demenz übergehen, ist es entscheidend, sie zu identifizieren. Dies sind die ersten Experimente, die diese Aufgabe unter ausschließlicher Verwendung von Sprache und Sprache als Input betrachten. Die Ergebnisse sind wieder ermutigend, sowohl bei der Verwendung von reiner Sprache als auch bei der Verwendung von Sprachdaten, die durch emotionale Fragen ausgelöst werden. Insgesamt sind die Ergebnisse sehr ermutigend und ermutigen zu weiterer Forschung, um sprachbasierte Biomarker für die Früherkennung und Überwachung dieser Erkrankungen zu etablieren und so das Leben der Patienten zu verbessern

Machine learning and brain imaging in psychosis

Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and despite any between-sites differences. Taken together, our findings suggest that machine learning is capable of distinguishing between cohorts of high risk subjects that later convert to psychosis and those that do not based on patterns of structural abnormalities that are present before disease onset. Our findings have some clinical implications in that machine learning-based approaches could advise or complement clinical decision-making in early intervention strategies in schizophrenia and related psychoses. Future work will be, however, required to tackle issues of reproducibility of early diagnostic biomarkers across research sites, where different assessment criteria and imaging equipment and protocols are used. In addition, future projects may also examine the diagnostic and prognostic value of multimodal neuroimaging data, possibly combined with other clinical, neurocognitive, genetic information

In Search of a Common Thread: Enhancing the LBD Workflow with a view to its Widespread Applicability

Literature-Based Discovery (LBD) research focuses on discovering implicit knowledge linkages in existing scientific literature to provide impetus to innovation and research productivity. Despite significant advancements in LBD research, previous studies contain several open problems and shortcomings that are hindering its progress. The overarching goal of this thesis is to address these issues, not only to enhance the discovery component of LBD, but also to shed light on new directions that can further strengthen the existing understanding of the LBD work ow. In accordance with this goal, the thesis aims to enhance the LBD work ow with a view to ensuring its widespread applicability. The goal of widespread applicability is twofold. Firstly, it relates to the adaptability of the proposed solutions to a diverse range of problem settings. These problem settings are not necessarily application areas that are closely related to the LBD context, but could include a wide range of problems beyond the typical scope of LBD, which has traditionally been applied to scientific literature. Adapting the LBD work ow to problems outside the typical scope of LBD is a worthwhile goal, since the intrinsic objective of LBD research, which is discovering novel linkages in text corpora is valid across a vast range of problem settings. Secondly, the idea of widespread applicability also denotes the capability of the proposed solutions to be executed in new environments. These `new environments' are various academic disciplines (i.e., cross-domain knowledge discovery) and publication languages (i.e., cross-lingual knowledge discovery). The application of LBD models to new environments is timely, since the massive growth of the scientific literature has engendered huge challenges to academics, irrespective of their domain. This thesis is divided into five main research objectives that address the following topics: literature synthesis, the input component, the discovery component, reusability, and portability. The objective of the literature synthesis is to address the gaps in existing LBD reviews by conducting the rst systematic literature review. The input component section aims to provide generalised insights on the suitability of various input types in the LBD work ow, focusing on their role and potential impact on the information retrieval cycle of LBD. The discovery component section aims to intermingle two research directions that have been under-investigated in the LBD literature, `modern word embedding techniques' and `temporal dimension' by proposing diachronic semantic inferences. Their potential positive in uence in knowledge discovery is veri ed through both direct and indirect uses. The reusability section aims to present a new, distinct viewpoint on these LBD models by verifying their reusability in a timely application area using a methodical reuse plan. The last section, portability, proposes an interdisciplinary LBD framework that can be applied to new environments. While highly cost-e cient and easily pluggable, this framework also gives rise to a new perspective on knowledge discovery through its generalisable capabilities. Succinctly, this thesis presents novel and distinct viewpoints to accomplish five main research objectives, enhancing the existing understanding of the LBD work ow. The thesis offers new insights which future LBD research could further explore and expand to create more eficient, widely applicable LBD models to enable broader community benefits.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Neocerebellar Kalman filter linguistic processor: from grammaticalization to transcranial magnetic stimulation

The present work introduces a synthesis of neocerebellar state estimation and feedforward control with multi-level language processing. The approach combines insights from clinical, imaging, and modelling work on the cerebellum with psycholinguistic and historical linguistic research. It finally provides the first experimental attempts towards the empirical validation of this synthesis, employing transcranial magnetic stimulation. A neuroanatomical locus traditionally seen as limited to lower sensorimotor functions, the cerebellum has, over the last decades, emerged as a widely accepted foundation of feedforward control and state estimation. Its cytoarchitectural homogeneity and diverse connectivity with virtually all parts of the central nervous system strongly support the idea of a uniform, domain-general cerebellar computation. Its reciprocal connectivity with language-related cortical areas suggests that this uniform cerebellar computation is also applied in language processing. Insight into the latter, however, remains an elusive desideratum; instead, research on cerebellar language functions is predominantly involved in the frontal cortical-like deficits (e.g. aphasias) seldom induced by cerebellar impairment. At the same time, reflections on cerebellar computations in language processing remain at most speculative, given the lack of discourse between cerebellar neuroscientists and psycholinguists. On the other hand, the fortunate contingency of the recent accommodation of these computations in psycholinguistic models provides the foundations for satisfying the desideratum above. The thesis thus formulates a neurolinguistic model whereby multi-level, predictive, associative linguistic operations are acquired and performed in neocerebello-cortical circuits, and are adaptively combined with cortico-cortical categorical processes. A broad range of psycholinguistic phenomena, involving, among others, "pragmatic normalization", "verbal/semantic illusions", associative priming, and phoneme restoration, are discussed in the light of recent findings on neocerebellar cognitive functions, and provide a rich research agenda for the experimental validation of the proposal. The hypothesis is then taken further, examining grammaticalization changes in the light of neocerebellar linguistic contributions. Despite a) the broad acceptance of routinization and automatization processes as the domain-general core of grammaticalization, b) the growing psycholinguistic research on routinized processing, and c) the evidence on neural circuits involved in automatization processes (crucially involving the cerebellum), interdisciplinary discourse remains strikingly poor. Based on the above, a synthesis is developed, whereby grammaticalization changes are introduced in routinized dialogical interaction as the result of maximized involvement of associative neocerebello-cortical processes. The thesis then turns to the first steps taken towards the verification of the hypothesis at hand. In view of the large methodological limitations of clinical research on cerebellar cognitive functions, the transcranial magnetic stimulation apparatus is employed instead, producing the very first linguistic experiments involving cerebellar stimulation. Despite the considerable technical difficulties met, neocerebellar loci are shown to be selectively involved in formal- and semantic-associative computations, with far-reaching consequences for neurolinguistic models of sentence processing. In particular, stimulation of the neocerebellar vermis is found to selectively enhance formal-associative priming in native speakers of English, and to disrupt, rather selectively, semantic-categorical priming in native speakers of Modern Greek, as well as to disrupt the practice-induced facilitation in processing repeatedly associated letter strings. Finally, stimulation of the right neocerebellar Crus I is found to enhance, quite selectively, semantic-associative priming in native speakers of English, while stimulation of the right neocerebellar vermis is shown to disrupt semantic priming altogether. The results are finally discussed in the light of a future research agenda overcoming the technical limitations met here