Investigation of neural activity in Schizophrenia during resting-state MEG : using non-linear dynamics and machine-learning to shed light on information disruption in the brain

Environ 25% de la population mondiale est atteinte de troubles psychiatriques qui sont typiquement associés à des problèmes comportementaux, fonctionnels et/ou cognitifs et dont les corrélats neurophysiologiques sont encore très mal compris. Non seulement ces dysfonctionnements réduisent la qualité de vie des individus touchés, mais ils peuvent aussi devenir un fardeau pour les proches et peser lourd dans l’économie d’une société. Cibler les mécanismes responsables du fonctionnement atypique du cerveau en identifiant des biomarqueurs plus robustes permettrait le développement de traitements plus efficaces. Ainsi, le premier objectif de cette thèse est de contribuer à une meilleure caractérisation des changements dynamiques cérébraux impliqués dans les troubles mentaux, plus précisément dans la schizophrénie et les troubles d’humeur. Pour ce faire, les premiers chapitres de cette thèse présentent, en intégral, deux revues de littératures systématiques que nous avons menées sur les altérations de connectivité cérébrale, au repos, chez les patients schizophrènes, dépressifs et bipolaires. Ces revues révèlent que, malgré des avancées scientifiques considérables dans l’étude de l’altération de la connectivité cérébrale fonctionnelle, la dimension temporelle des mécanismes cérébraux à l’origine de l’atteinte de l’intégration de l’information dans ces maladies, particulièrement de la schizophrénie, est encore mal comprise. Par conséquent, le deuxième objectif de cette thèse est de caractériser les changements cérébraux associés à la schizophrénie dans le domaine temporel. Nous présentons deux études dans lesquelles nous testons l’hypothèse que la « disconnectivité temporelle » serait un biomarqueur important en schizophrénie. Ces études explorent les déficits d’intégration temporelle en schizophrénie, en quantifiant les changements de la dynamique neuronale dite invariante d’échelle à partir des données magnétoencéphalographiques (MEG) enregistrés au repos chez des patients et des sujets contrôles. En particulier, nous utilisons (1) la LRTCs (long-range temporal correlation, ou corrélation temporelle à longue-distance) calculée à partir des oscillations neuronales et (2) des analyses multifractales pour caractériser des modifications de l’activité cérébrale arythmique. Par ailleurs, nous développons des modèles de classification (en apprentissage-machine supervisé) pour mieux cerner les attributs corticaux et sous-corticaux permettant une distinction robuste entre les patients et les sujets sains. Vu que ces études se basent sur des données MEG spontanées enregistrées au repos soit avec les yeux ouvert, ou les yeux fermées, nous nous sommes par la suite intéressés à la possibilité de trouver un marqueur qui combinerait ces enregistrements. La troisième étude originale explore donc l’utilité des modulations de l’amplitude spectrale entre yeux ouverts et fermées comme prédicteur de schizophrénie. Les résultats de ces études démontrent des changements cérébraux importants chez les patients schizophrènes au niveau de la dynamique d’invariance d’échelle. Elles suggèrent une dégradation du traitement temporel de l’information chez les patients, qui pourrait être liée à leurs symptômes cognitifs et comportementaux. L’approche multimodale de cette thèse, combinant la magétoencéphalographie, analyses non-linéaires et apprentissage machine, permet de mieux caractériser l’organisation spatio-temporelle du signal cérébrale au repos chez les patients atteints de schizophrénie et chez des individus sains. Les résultats fournissent de nouvelles preuves supportant l’hypothèse d’une « disconnectivité temporelle » en schizophrénie, et étendent les recherches antérieures, en explorant la contribution des structures cérébrales profondes et en employant des mesures non-linéaires avancées encore sous-exploitées dans ce domaine. L’ensemble des résultats de cette thèse apporte une contribution significative à la quête de nouveaux biomarqueurs de la schizophrénie et démontre l’importance d’élucider les altérations des propriétés temporelles de l’activité cérébrales intrinsèque en psychiatrie. Les études présentées offrent également un cadre méthodologique pouvant être étendu à d’autres psychopathologie, telles que la dépression.Psychiatric disorders affect nearly a quarter of the world’s population. These typically bring about debilitating behavioural, functional and/or cognitive problems, for which the underlying neural mechanisms are poorly understood. These symptoms can significantly reduce the quality of life of affected individuals, impact those close to them, and bring on an economic burden on society. Hence, targeting the baseline neurophysiology associated with psychopathologies, by identifying more robust biomarkers, would improve the development of effective treatments. The first goal of this thesis is thus to contribute to a better characterization of neural dynamic alterations in mental health illnesses, specifically in schizophrenia and mood disorders. Accordingly, the first chapter of this thesis presents two systematic literature reviews, which investigate the resting-state changes in brain connectivity in schizophrenia, depression and bipolar disorder patients. Great strides have been made in neuroimaging research in identifying alterations in functional connectivity. However, these two reviews reveal a gap in the knowledge about the temporal basis of the neural mechanisms involved in the disruption of information integration in these pathologies, particularly in schizophrenia. Therefore, the second goal of this thesis is to characterize the baseline temporal neural alterations of schizophrenia. We present two studies for which we hypothesize that the resting temporal dysconnectivity could serve as a key biomarker in schizophrenia. These studies explore temporal integration deficits in schizophrenia by quantifying neural alterations of scale-free dynamics using resting-state magnetoencephalography (MEG) data. Specifically, we use (1) long-range temporal correlation (LRTC) analysis on oscillatory activity and (2) multifractal analysis on arrhythmic brain activity. In addition, we develop classification models (based on supervised machine-learning) to detect the cortical and sub-cortical features that allow for a robust division of patients and healthy controls. Given that these studies are based on MEG spontaneous brain activity, recorded at rest with either eyes-open or eyes-closed, we then explored the possibility of finding a distinctive feature that would combine both types of resting-state recordings. Thus, the third study investigates whether alterations in spectral amplitude between eyes-open and eyes-closed conditions can be used as a possible marker for schizophrenia. Overall, the three studies show changes in the scale-free dynamics of schizophrenia patients at rest that suggest a deterioration of the temporal processing of information in patients, which might relate to their cognitive and behavioural symptoms. The multimodal approach of this thesis, combining MEG, non-linear analyses and machine-learning, improves the characterization of the resting spatiotemporal neural organization of schizophrenia patients and healthy controls. Our findings provide new evidence for the temporal dysconnectivity hypothesis in schizophrenia. The results extend on previous studies by characterizing scale-free properties of deep brain structures and applying advanced non-linear metrics that are underused in the field of psychiatry. The results of this thesis contribute significantly to the identification of novel biomarkers in schizophrenia and show the importance of clarifying the temporal properties of altered intrinsic neural dynamics. Moreover, the presented studies offer a methodological framework that can be extended to other psychopathologies, such as depression

Prosody beyond pitch and emotion in speech and music: evidence from right hemisphere brain damage and congenital amusia

This dissertation examines the relationship of prosodic processing in language and music from a new perspective, considering acoustic features that have not been studied before in the framework of the parallel study of language and music. These features are argued to contribute to the effect of ‘expressiveness’ which is here defined as the combination of the acoustic features (variation in duration, pitch, loudness, and articulation) that results in aesthetic appreciation of the linguistic and the musical acoustic stream and which is distinct from pitch, emotional and pragmatic prosody as well as syntactic structure. The present investigation took a neuropsychological approach, comparing the performance of a right temporo-parietal stroke patient IB; a congenitally amusic individual, BZ; and 24 control participants with and without musical training. Apart from the main focus on the perception of ‘expressiveness’, additional aspects of language and music perception were studied. A new battery was designed that consisted of 8 tasks; ‘speech prosody detection’, ‘expressive speech prosody’, ‘expressive music prosody’, ‘emotional speech prosody’, ‘emotional music prosody, ‘speech pitch’, ‘speech rate’, and ‘music tempo’. These tasks addressed both theoretical and methodological issues in this comparative cognitive framework. IB’s performance on the expressive speech prosody task revealed a severe perceptual impairment, whereas his performance on the analogous music task examining ‘expressiveness’ was unimpaired. BZ also performed successfully on the same music task despite being characterised as congenital amusic by an earlier study. Musically untrained controls also had a successful performance. The data from IB suggest that speech and music stimuli encompassing similar features are not necessarily processed by the same mechanisms. These results can have further implications for the approach to the relationship of language and music within the study of cognitive deficits

Language impairments in people with autoimmune neurological diseases:A scoping review

INTRODUCTION: Autoimmune neurological diseases (ANDs) are a specific type of autoimmune disease that affect cells within the central and peripheral nervous system. ANDs trigger various physical/neuropsychiatric symptoms. However, language impairments in people with ANDs are not well characterized. Here we aimed to determine the kinds of language impairment that most commonly emerge in 10 ANDs, the characteristics of the patients (demographic, neurological damage), and the assessment methods used.METHODS: We followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR). PubMed and Google Scholar were searched. We used a list of search terms containing 10 types of ANDs (e.g., multiple sclerosis, acute disseminated encephalomyelitis) in combination with the terms aphasia, dysphasia, fluency, language, listening, morphology, phonology, pragmatics, reading, semantics, speaking, syntax, writing. The reference lists and citations of the relevant papers were also investigated. The type of AND, patient characteristics, neurological damage and examination technique, language tests administered, and main findings were noted for each study meeting the inclusion criteria.RESULTS: We found 171 studies meeting our inclusion criteria. These comprised group studies and case studies. Language impairments differed largely among types of ANDs. Neurological findings were mentioned in most of the papers, but specific language tests were rarely used.CONCLUSIONS: Language symptoms in people with ANDs are commonly reported. These are often not full descriptions or only focus on specific time points in the course of the disease. Future research needs to assess specific language functions in people with ANDs and relate their language impairments to brain damage at different stages of disease evolution.</p

VALIDATION OF A MODEL OF SENSORIMOTOR INTEGRATION WITH CLINICAL BENEFITS

Healthy sensorimotor integration – or how our touch influences our movements – is critical to efficiently interact with our environment. Yet, many aspects of this process are still poorly understood. Importantly, several movement disorders are often considered as originating from purely motor impairments, while a sensory origin could also lead to a similar set of symptoms. To alleviate these issues, we hereby propose a novel biologically-based model of the sensorimotor loop, known as the SMILE model. After describing both the functional, and the corresponding neuroanatomical versions of the SMILE, we tested several aspects of its motor component through functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). Both experimental studies resulted in coherent outcomes with respect to the SMILE predictions, but they also provided novel scientific outcomes about such broad topics as the sub-phases of motor imagery, the neural processing of bodily representations, or the extend of the role of the extrastriate body area. In the final sections of this manuscript, we describe some potential clinical application of the SMILE. The first one presents the identification of plausible neuroanatomical origins for focal hand dystonia, a yet poorly understood sensorimotor disorder. The last chapter then covers possible improvements on brain-machine interfaces, driven by a better understanding of the sensorimotor system. -- La façon dont votre sens du toucher et vos mouvements interagissent est connue sous le nom d’intégration sensorimotrice. Ce procédé est essentiel pour une interaction normale avec tout ce qui nous entoure. Cependant, plusieurs aspects de ce processus sont encore méconnus. Plus important encore, l’origine de certaines déficiences motrices encore trop peu comprises sont parfois considérées comme purement motrice, alors qu’une origine sensorielle pourrait mener à un même ensemble de symptômes. Afin d’améliorer cette situation, nous proposons ici un nouveau modèle d’intégration sensorimotrice, dénommé « SMILE », basé sur les connaissances de neurobiologie actuelles. Dans ce manuscrit, nous commençons par décrire les caractéristiques fonctionnelles et neuroanatomiques du SMILE. Plusieurs expériences sont ensuite effectuées, via l’imagerie par résonance magnétique fonctionnelle (IRMf), et la stimulation magnétique transcranienne (SMT), afin de tester différents aspects de la composante motrice du SMILE. Si les résultats de ces expériences corroborent les prédictions du SMILE, elles ont aussi mis en évidences d’autres résultats scientifiques intéressants et novateurs, dans des domaines aussi divers que les sous-phases de l’imagination motrice, les processus cérébraux liés aux représentations corporelles, ou encore l’extension du rôle de l’extrastriate body area. Dans les dernières parties de ce manuscrit, nous dévoilons quelques applications cliniques potentielles de notre modèle. Nous utilisons le SMILE afin de proposer deux origines cérébrales plausibles de la dystonie focale de la main. Le dernier chapitre présente comment certaines technologies existantes, telles que les interfaces cerveaux-machines, pourraient bénéficier d’une meilleure compréhension du système sensorimoteur

The words of the body: psychophysiological patterns in dissociative narratives

Trauma has severe consequences on both psychological and somatic levels, even affecting the genetic expression and the cell\u2019s DNA repair ability. A key mechanism in the understanding of clinical disorders deriving from trauma is identified in dissociation, as a primitive defense against the fragmentation of the self originated by overwhelming experiences. The dysregulation of the interpersonal patterns due to the traumatic experience and its detrimental effects on the body are supported by influent neuroscientific models such as Damasio\u2019s somatic markers and Porges\u2019 polyvagal theory. On the basis of these premises, and supported by our previous empirical observations on 40 simulated clinical sessions, we will discuss the longitudinal process of a brief psychodynamic psychotherapy (16 sessions, weekly frequency) with a patient who suffered a relational trauma. The research design consists of the collection of self-report and projective tests, pre-post therapy and after each clinical session, in order to assess personality, empathy, clinical alliance and clinical progress, along with the verbatim analysis of the transcripts trough the Psychotherapy Process Q-Set and the Collaborative Interactions Scale. Furthermore, we collected simultaneous psychophysiological measures of the therapeutic dyad: skin conductance and hearth rate. Lastly, we employed a computerized analysis of non-verbal behaviors to assess synchrony in posture and gestures. These automated measures are able to highlight moments of affective concordance and discordance, allowing for a deep understanding of the mutual regulations between the patient and the therapist. Preliminary results showed that psychophysiological changes in dyadic synchrony, observed in body movements, skin conductance and hearth rate, occurred within sessions during the discussion of traumatic experiences, with levels of attunement that changed in both therapist and the patient depending on the quality of the emotional representation of the experience. These results go in the direction of understanding the relational process in trauma therapy, using an integrative language in which both clinical and neurophysiological knowledge may take advantage of each other

Exploring the Diagnosis of Frontotemporal Dementia by Analyzing Neuropsychological Data With K-Means Clustering

Background: Differential diagnosis of dementia syndromes is difficult. Treatments are available for Alzheimer’s dementia (AD), but effects do not translate to other dementia syndromes. Therefore, early differential diagnosis is necessary to administer appropriate interventions and to boost drug development and research of therapeutic strategies that may lower patient and care-giver burden. The focus of the current study is to disentangle the clinical picture of frontotemporal dementia (FTD) from AD spectrum disorders. Findings could support an early, cheap, and more accurate diagnosis. Methods: K-means clustering, an unsupervised machine learning algorithm, was used on neuropsychological data from neurologic patients of the FTLD consortium databank. The analysis was performed twice, once including only neuropsychological test scores and a second time combining the neuropsychological variables with questionnaire scores assessing behavioral changes. In total n = 484 and n = 469 participants were included in the analysis with and without questionnaires, respectively. Participants included were either healthy controls with no family relation to patients in the dataset, or patients diagnosed with one of the following dementia disorders: AD, a behavioral variant of FTD (bvFTD), or one of three possible primary progressive aphasia (PPA) syndromes - a semantic variant (svPPA), a non-fluent variant (nfvPPA) or a logopenic variant (lvPPA). Results: Agreement of results from the various analyses performed was relatively high. Homogeneous clusters of diagnostic groups emerged. Homogeneity seemed higher for bvFTD and svPPA than for the other patient groups. NfvPPA and lvPPA patients were particularly likely to cluster together. Exploring neuropsychological patterns of cluster results demonstrated high variability between patients of the same diagnostic groups which could partly be explained by differences in disease severity. Tests that might prove particularly relevant to distinguish diagnostic subgroups are the FTLD-CDR sub-scores, the repeat and point task as well as questionnaires assessing apathy. Conclusion: K-means clustering proved to be a useful technique to explore various diagnostic syndromes that show overlapping clinical pictures. This study helped to formulate specific hypotheses based on the observation of patterns in multidimensional data. Disease severity showed to impact k-means clustering results considerably and should therefore be accounted for in future studies. Future studies will need to test the formulated hypotheses and inspect the meaning of impure clusters. Keywords: k-means clustering, frontotemporal dementia, primary progressive aphasia, Alzheimer’s dementia, differential diagnosis, neuropsychological testsBackground: Differential diagnosis of dementia syndromes is difficult. Treatments are available for Alzheimer’s dementia (AD), but effects do not translate to other dementia syndromes. Therefore, early differential diagnosis is necessary to administer appropriate interventions and to boost drug development and research of therapeutic strategies that may lower patient and care-giver burden. The focus of the current study is to disentangle the clinical picture of frontotemporal dementia (FTD) from AD spectrum disorders. Findings could support an early, cheap, and more accurate diagnosis. Methods: K-means clustering, an unsupervised machine learning algorithm, was used on neuropsychological data from neurologic patients of the FTLD consortium databank. The analysis was performed twice, once including only neuropsychological test scores and a second time combining the neuropsychological variables with questionnaire scores assessing behavioral changes. In total n = 484 and n = 469 participants were included in the analysis with and without questionnaires, respectively. Participants included were either healthy controls with no family relation to patients in the dataset, or patients diagnosed with one of the following dementia disorders: AD, a behavioral variant of FTD (bvFTD), or one of three possible primary progressive aphasia (PPA) syndromes - a semantic variant (svPPA), a non-fluent variant (nfvPPA) or a logopenic variant (lvPPA). Results: Agreement of results from the various analyses performed was relatively high. Homogeneous clusters of diagnostic groups emerged. Homogeneity seemed higher for bvFTD and svPPA than for the other patient groups. NfvPPA and lvPPA patients were particularly likely to cluster together. Exploring neuropsychological patterns of cluster results demonstrated high variability between patients of the same diagnostic groups which could partly be explained by differences in disease severity. Tests that might prove particularly relevant to distinguish diagnostic subgroups are the FTLD-CDR sub-scores, the repeat and point task as well as questionnaires assessing apathy. Conclusion: K-means clustering proved to be a useful technique to explore various diagnostic syndromes that show overlapping clinical pictures. This study helped to formulate specific hypotheses based on the observation of patterns in multidimensional data. Disease severity showed to impact k-means clustering results considerably and should therefore be accounted for in future studies. Future studies will need to test the formulated hypotheses and inspect the meaning of impure clusters. Keywords: k-means clustering, frontotemporal dementia, primary progressive aphasia, Alzheimer’s dementia, differential diagnosis, neuropsychological test

Primary progressive aphasia : neuropsychological analysis and evolution

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2015Frontotemporal lobar degeneration (FTLD) is the second leading cause of early-onset ( 2) revealed some clusters composed mostly by nonfluent or by semantic PPA cases. However, we could not evidence any group chiefly composed of logopenic PPA cases. Hence, findings obtained with the application of unsupervised data mining approaches do not clearly support a logopenic PPA. However further, supervised learning studies may indicate distinct results. Behaviour changes may occur early in PPA but the frequency of these symptoms across the three variants is still controversial. In the third study, 94 consecutive PPA patients (26 nonfluent, 36 semantic, 32 logopenic) underwent language and neuropsychological assessments. The presence of behavioural changes was ascertained by semi-structured informant-based interviews using the Blessed Dementia Rating Scale. Eighty-two percent of the cases endorsed at least one behaviour change. Nonfluent patients presented significantly more behaviour changes and scored more often (46.2%) the item “hobbies relinquished” when compared to logopenic patients. These differences in behaviour symptoms probably reflect distinct underlying neurodegenerative diseases. PPA is a neurodegenerative disorder with no effective pharmacological treatment. Cognition-based interventions are adequate alternatives, but their benefit has not been thoroughly explored. The aim of this last investigation was to study the effect of speech and language therapy (SLT) on naming ability in PPA. An open parallel prospective longitudinal study involving two centers was designed to compare patients with PPA submitted to SLT (1 h/week for 11 months, on average) with patients receiving no therapy. Twenty patients were enrolled and undertook baseline language and neuropsychological assessments; among them, 10 received SLT and 10 constituted an age- and education-matched historical control group. The primary outcome measure was the change in group mean performance on the Snodgrass and Vanderwart Naming Test between baseline and follow-up assessments. Intervention and control groups did not significantly differ on demographic and clinical variables at baseline. A mixed repeated measures ANOVA revealed a significant main effect of therapy (F(1,18) = 10.763; p = 0.005) on the performance on the Snodgrass and Vanderwart Naming Test. Although limited by a non-randomized open study design with a historical control group, the present study suggests that SLT may have a benefit in PPA, and it should prompt a randomized, controlled, rater-blind clinical trial. Conclusion: Despite the recent harmonization efforts, the delineation of certain PPA variants is still controversial. The present results show that neuropsychology is a key instrument not only for the clear definition of PPA subtypes but also for the study of the abnormal mechanisms and features underlying the main forms of PPA. Moreover, a neuropsychological approach to disease management seems to be feasible. Specifically, SLT emerges as an alternative and adequate approach to tackle the increasing language deficits experienced in all PPA phenotypes for some time. The emergence of promising disease-modifying therapies in the context of FTLD, in association with these cognitive-based interventions, will certainly be the future of PPA disease management

Predictive cognition in dementia: the case of music

The clinical complexity and pathological diversity of neurodegenerative diseases impose immense challenges for diagnosis and the design of rational interventions. To address these challenges, there is a need to identify new paradigms and biomarkers that capture shared pathophysiological processes and can be applied across a range of diseases. One core paradigm of brain function is predictive coding: the processes by which the brain establishes predictions and uses them to minimise prediction errors represented as the difference between predictions and actual sensory inputs. The processes involved in processing unexpected events and responding appropriately are vulnerable in common dementias but difficult to characterise. In my PhD work, I have exploited key properties of music – its universality, ecological relevance and structural regularity – to model and assess predictive cognition in patients representing major syndromes of frontotemporal dementia – non-fluent variant PPA (nfvPPA), semantic-variant PPA (svPPA) and behavioural-variant FTD (bvFTD) - and Alzheimer’s disease relative to healthy older individuals. In my first experiment, I presented patients with well-known melodies containing no deviants or one of three types of deviant - acoustic (white-noise burst), syntactic (key-violating pitch change) or semantic (key-preserving pitch change). I assessed accuracy detecting melodic deviants and simultaneously-recorded pupillary responses to these deviants. I used voxel-based morphometry to define neuroanatomical substrates for the behavioural and autonomic processing of these different types of deviants, and identified a posterior temporo-parietal network for detection of basic acoustic deviants and a more anterior fronto-temporo-striatal network for detection of syntactic pitch deviants. In my second chapter, I investigated the ability of patients to track the statistical structure of the same musical stimuli, using a computational model of the information dynamics of music to calculate the information-content of deviants (unexpectedness) and entropy of melodies (uncertainty). I related these information-theoretic metrics to performance for detection of deviants and to ‘evoked’ and ‘integrative’ pupil reactivity to deviants and melodies respectively and found neuroanatomical correlates in bilateral dorsal and ventral striatum, hippocampus, superior temporal gyri, right temporal pole and left inferior frontal gyrus. Together, chapters 3 and 4 revealed new hypotheses about the way FTD and AD pathologies disrupt the integration of predictive errors with predictions: a retained ability of AD patients to detect deviants at all levels of the hierarchy with a preserved autonomic sensitivity to information-theoretic properties of musical stimuli; a generalized impairment of surprise detection and statistical tracking of musical information at both a cognitive and autonomic levels for svPPA patients underlying a diminished precision of predictions; the exact mirror profile of svPPA patients in nfvPPA patients with an abnormally high rate of false-alarms with up-regulated pupillary reactivity to deviants, interpreted as over-precise or inflexible predictions accompanied with normal cognitive and autonomic probabilistic tracking of information; an impaired behavioural and autonomic reactivity to unexpected events with a retained reactivity to environmental uncertainty in bvFTD patients. Chapters 5 and 6 assessed the status of reward prediction error processing and updating via actions in bvFTD. I created pleasant and aversive musical stimuli by manipulating chord progressions and used a classic reinforcement-learning paradigm which asked participants to choose the visual cue with the highest probability of obtaining a musical ‘reward’. bvFTD patients showed reduced sensitivity to the consequence of an action and lower learning rate in response to aversive stimuli compared to reward. These results correlated with neuroanatomical substrates in ventral and dorsal attention networks, dorsal striatum, parahippocampal gyrus and temporo-parietal junction. Deficits were governed by the level of environmental uncertainty with normal learning dynamics in a structured and binarized environment but exacerbated deficits in noisier environments. Impaired choice accuracy in noisy environments correlated with measures of ritualistic and compulsive behavioural changes and abnormally reduced learning dynamics correlated with behavioural changes related to empathy and theory-of-mind. Together, these experiments represent the most comprehensive attempt to date to define the way neurodegenerative pathologies disrupts the perceptual, behavioural and physiological encoding of unexpected events in predictive coding terms

Gender Differences in Student Learning: A Review of the Literature from the Neuroscience and Psychology Perspectives

Literature from the fields of psychology and neuroscience were examined to establish what scientifically based information was available regarding gender differences. Myths of the past, psychological and neuroscientific perspectives, gender specific differences for students with genetic and metabolic-based exceptionalities, gender specific changes in behavior through maturation, and the educational implications for gender differences were included. The results indicated that children show developmental differences in expression of emotion, metacognition, and cognition, and the developmental differences are influenced by gender. The implications for educational practices are that curriculum, instruction, and assessment can be aligned to meet the unique need of both male and female students