408 research outputs found

    Apraxias in the Diagnosis of Frontotemporal Dementia and Alzheimer’s Disease

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    Alzheimerin tauti alkaa tyypillisesti muistihĂ€iriöllĂ€, mutta työikĂ€isillĂ€ alkuoire on usein jokin muu neuropsykologinen hĂ€iriö. Yksi nĂ€istĂ€ on apraksia, joka tarkoittaa aivovaurion aiheuttamaa puutosta tahdonalaisissa liikkeissĂ€ ja niiden ymmĂ€rtĂ€misessĂ€. TyöikĂ€isillĂ€ Alzheimerin tauti tunnistetaan epĂ€tyypillisyytensĂ€ vuoksi hitaammin kuin iĂ€kkĂ€illĂ€ potilailla ja sekoitetaan herkĂ€sti esimerkiksi otsa-ohimolohkorappeumien aiheuttamiin dementioihin tai psykiatrisiin oireyhtymiin. Apraksioiden yleisyydestĂ€ ja muodoista otsa-ohimolohkorappeumissa ei ole kokonaiskuvaa, joten niiden selvittĂ€minen oli vĂ€itöstyön ensimmĂ€inen tavoite. Toinen tavoite oli mÀÀrittÀÀ, millĂ€ tarkkuudella apraksioiden arviointiin kehitetty testi tunnistaa varhain alkavan Alzheimerin taudin. Systemaattisessa kirjallisuuskatsauksessa ilmeni, ettĂ€ kuhunkin otsa-ohimolohkorappeumien dementiamuotoon voi kehittyĂ€ oma apraksiaprofiilinsa: KĂ€ytösjohtoisessa tautimuodossa selkein löydös oli kasvoapraksia, joka auttoi erottelussa Alzheimerin taudista. YlĂ€raaja-apraksia ilmeni hienovaraisempana. Sujumattomassa afasiassa raportoitiin sekĂ€ kasvo- ettĂ€ raaja-apraksiaa ja lisĂ€ksi puheapraksiaa. Semanttisessa dementiassa ei tyypillisesti havaittu mitÀÀn nĂ€istĂ€ vaan liikkeiden merkityksen ja esineiden kĂ€ytön ymmĂ€rryksen hĂ€iriötĂ€. LogopenisessĂ€ afasiassa ei ilmennyt kasvo-apraksiaa, ja raaja-apraksia oli samankaltainen kuin Alzheimerin taudissa. Dementia Apraxia Testin erottelutarkkuutta tutkittiin 50–70-vuotiaiden Alzheimer-potilaiden, psykiatristen potilaiden ja terveiden verrokkien vĂ€lillĂ€. Testin raajapistemÀÀrĂ€ erotteli Alzheimer-potilaat terveistĂ€ herkkyydellĂ€ 92% ja tarkkuudella 100% (Youden-arvo .92). Potilasryhmien vĂ€lillĂ€ erotteluherkkyys oli 83% ja -tarkkuus 100% (Youden-arvo .83). KasvopistemÀÀrĂ€ ja muistitestit olivat epĂ€tarkempia: ne erottelivat oikein 70–75% ja 66–78% potilaista. Otsa-ohimolohkorappeumien dementiamuotoihin nĂ€yttÀÀ siis kehittyvĂ€n apraksiaprofiileja, joiden kliininen pĂ€tevyys ja erotusdiagnostinen arvo on relevantti jatkotutkimuksen aihe. Varhain alkavan Alzheimerin taudin tunnistukseen Dementia Apraxia Test tuottaa lisĂ€hyötyĂ€ erityisesti eroteltaessa psykiatrisperĂ€isistĂ€ muistihĂ€iriöistĂ€. Jatkossa tarvitaan tietoa testin toimivuudesta muissa etenevissĂ€ aivosairauksissa ja psykoosisairauksissa.Early dementia is challenging to diagnose in late middle age if the disease debuts with symptoms other than memory disturbance. Alzheimer’s disease, the most prevalent type of dementia, is commonly confused with psychiatric syndromes and frontotemporal dementia. One of the atypical symptoms of Alzheimer’s disease is apraxia, a deficit in voluntary action. Frontotemporal dementia may also involve apraxia, but there are no integrative data on the topic. This thesis synthesized current evidence on apraxias in frontotemporal dementia and explored whether an apraxia test could support the detection of Alzheimer’s disease in middle age. The systematic literature review suggested specific apraxia profiles for each of the four clinical variants of frontotemporal dementia. The behavioural variant involved early face apraxia, a feature that enabled differentiation from Alzheimer’s disease. Limb apraxia was present but subtler than in Alzheimer’s disease. The nonfluent variant typically showed remarkable face and limb apraxia, often in combination with apraxia of speech. The semantic variant showed preserved production of simple gestures but impaired understanding of tool use and gestures. The apraxia profile of the logopenic variant resembled that of Alzheimer’s disease, with remarkable limb apraxia and spared face praxis. The diagnostic accuracy of the Dementia Apraxia Test was estimated between samples of 50–70-year-old Alzheimer’s disease patients, psychiatric patients and healthy control participants. The limb praxis scale of the test distinguished between the Alzheimer’s disease group and the healthy participants, with 92% sensitivity and 100% specificity (Youden index .92). Between the Alzheimer’s disease and psychiatric groups, the limb scale reached 83% sensitivity and 100% specificity (Youden index 0.83). The face scale and memory tests were diagnostically less accurate, correctly classifying 70–75% and 66–78% of the patients, respectively. Apraxia profiles may thus support differentiation between early dementias, a hypothesis that requires future clinical validation. The Dementia Apraxia Test accurately detects Alzheimer’s disease in middle-aged populations and is especially recommended for clinical use to identify psychiatric aetiology in patients with memory disturbances. The test’s performance in other neurodegenerative diseases and psychotic conditions should be investigated next

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Brain morphological and functional correlates of genetic, psychological, prenatal and prodromal risk for major mental disorders and their behavioural links

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    Cross-sectional mri-studies comparing psychiatric patients with healthy individuals have shown that patients show brain morphometric as well as functional changes. However, it is unclear whether these are pathological factors or whether these neurobiological changes are simply a risk factor for mental disorders, a consequence of therapy, only occur in certain subgroups. Therefore, the influence of a broad spectrum of different risk factors for mental disorders on brain morphometry as well as function was investigated in the present study: polygenic risk scores for psychiatric disorders, temporal perspective, shortened prenatal development as well as an extremely high risk for the development of psychosis. It can be shown that these risk factors significantly influence brain structural parameters as well as brain function. Some of these changes also correlated with behavioural changes such as poorer cognitive performance. These behavioural correlates could be valuable diagnostic or prognostic markers and could also be important research targets for the development of new therapeutic approaches

    Contributions of Human Prefrontal Cortex to the Recogitation of Thought

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    Human beings have a unique ability to not only verbally articulate past and present experiences, as well as potential future ones, but also evaluate the mental representations of such things. Some evaluations do little good, in that they poorly reflect facts, create needless emotional distress, and contribute to the obstruction of personal goals, whereas some evaluations are the converse: They are grounded in logic, empiricism, and pragmatism and, therefore, are functional rather than dysfunctional. The aim of non-pharmacological mental health interventions is to revise dysfunctional thoughts into more adaptive, healthier ones; however, the neurocognitive mechanisms driving cognitive change have hitherto remained unclear. Therefore, this thesis examines the role of the prefrontal cortex (PFC) in this aspect of human higher cognition using the relatively new method of functional near-infrared spectroscopy (fNIRS). Chapter 1 advances recogitation as the mental ability on which cognitive restructuring largely depends, concluding that, as a cognitive task, it is a form of open-ended human problem-solving that uses metacognitive and reasoning faculties. Because these faculties share similar executive resources, Chapter 2 discusses the systems in the brain involved in controlled information processing, specifically the nature of executive functions and their neural bases. Chapter 3 builds on these ideas to propose an information-processing model of recogitation, which predicts the roles of different subsystems localized within the PFC and elsewhere in the context of emotion regulation. This chapter also highlights several theoretical and empirical challenges to investigating this neurocognitive theory and proposes some solutions, such as to use experimental designs that are more ecologically valid. Chapter 4 focuses on a neuroimaging method that is best suited to investigating questions of spatial localization in ecological experiments, namely functional near-infrared spectroscopy (fNIRS). Chapter 5 then demonstrates a novel approach to investigating the neural bases of interpersonal interactions in clinical settings using fNIRS. Chapter 6 explores physical activity as a ‘bottom-up’ approach to upregulating the PFC, in that it might help clinical populations with executive deficits to regulate their mental health from the ‘top-down’. Chapter 7 addresses some of the methodological issues of investigating clinical interactions and physical activity in more naturalistic settings by assessing an approach to recovering functional events from observed brain data. Chapter 8 draws several conclusions about the role of the PFC in improving psychological as well as physiological well-being, particularly that rostral PFC is inextricably involved in the cognitive effort to modulate dysfunctional thoughts, and proposes some important future directions for ecological research in cognitive neuroscience; for example, psychotherapy is perhaps too physically stagnant, so integrating exercise into treatment environments might boost the effectiveness of intervention strategies

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Mapping & decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG

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    Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain–computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17–25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke

    Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

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    Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
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