408 research outputs found
Mental stimulation and multimodal trials to prevent cognitive impairment and Alzheimer Ìs disease
Theoretical models of dynamic biomarkers underlying the development of AlzheimerÂŽs
Disease (AD) acknowledge that there is inter-individual variability in the cognitive
performance associated with any level of AD pathology. Mentally stimulating activities
such as schooling, occupation, and leisure activities, may contribute to this variability,
but it is yet unclear how this can be best assessed, and how such effects can vary
across AD severity and among individuals at-risk for cognitive impairment. The
association between mental stimulation and cognitive performance also suggests that it
is important to account for mental stimulation levels in randomized clinical trials (RCTs)
comparing rates of cognitive change between interventions (i.e., drugs, lifestyle
interventions) and controls. The aim of this thesis was to investigate a) how pre-existing
levels of occupational complexity affect the cognitive outcomes of a multimodal
lifestyle-based RCT among older adults at increased risk for dementia based on a
validated risk score b) if occupational complexity is associated to cognitive
performance among individuals at-risk for dementia, including individuals in the early
stages of symptomatic AD (prodromal AD) and c) if occupational complexity is
associated with resilience to AD pathology, measured with validated biomarkers and
neuroimaging among individuals at-risk for cognitive impairment and with prodromal AD.
The four studies in this thesis were based on data from the Finnish Geriatric Intervention
Study to Prevent Cognitive Impairment and Disability (FINGER), the Karolinska University
Hospital electronic database and biobank for clinical research (GEDOC) and The
Multimodal Prevention Trial for AlzheimerÂŽs Disease (MIND-ADmini).
Study I. This study used data from the FINGER study (N=1026) to investigate if preexisting
levels of occupational complexity were associated with cognitive function at
baseline, and if occupational complexity was associated with the rate of change in
cognition during the 2-year intervention period. For all measures of occupational
complexity, higher levels of complexity were associated with better cognitive outcomes
at baseline. Occupational complexity was not associated with the rate of cognitive
change during the intervention, except for the executive function outcome, for which
higher levels of complexity with data predicted increased improvement ((Ă[SE]:
.028[.014], p=.044).
Study II. This study used data from the FINGER neuroimaging cohort, to investigate if the
association between occupational complexity and cognition was moderated by
measures of brain integrity, both in terms of magnetic resonance imaging (MRI, N=126)
and Pittsburgh-B Compound â Positron Emission Tomography (PiB-PET, N=41). The
results showed that higher levels of occupational complexity were associated with
better cognitive performance for some outcomes after adjusting for Alzheimerâs Disease
Signature (ADS) and medial temporal atrophy (MTA). However, for most types of
neuropathology and cognitive outcomes, moderation effects indicated that higher
occupational complexity levels were associated with better cognitive performance only
in people with higher brain integrity, suggesting lack of occupational complexity-related
resilience mechanisms.
Study III. This study investigated the association between mental stimulation
(occupational complexity and education) and validated AD biomarkers, AÎČ1â42, p-tau
and t-tau measured in cerebrospinal fluid (CSF). Using data from the GEDOC database,
174 individuals with prodromal AD were included, and analyses were adjusted for
cognitive function. The results indicated that both higher occupational complexity and
education were associated with higher levels of p-tau and t-tau. For education the
association with tau pathology was age dependent. No association was found with AÎČ1â
42. This suggests that higher education and occupational complexity may provide
resilience against tau-related pathology in prodromal AD.
Study IV. This study used data from FINGER, GEDOC, and MIND-ADmini, thus including a
total of 1410 individuals, 1207 at-risk for dementia and 203 with Prodromal AD. The aim
was to to compare the two most common rating systems for occupational complexity,
the Occupation Information Network (O*NET) and the Dictionary of Occupational Titles
(DOT) and assess if there was an association between occupational complexity and
episodic memory performance among individuals at-risk for dementia. The study found
that higher occupational complexity was only associated with memory performance in
the FINGER cohort but not the two prodromal AD cohorts. The correlation between the
two rating systems was moderate to strong, and highly significant (Spearmanâs rho =
0.5-0.6, p <.001).
Conclusions. Higher levels of Occupational complexity are associated with better
cognitive performance among older individuals at-risk for dementia (and with no
substantial cognitive impairment), but does not affect the intervention effect in the
FINGER multidomain lifestyle-based RCT, apart from the effect on executive function.
Occupational complexity does not seem to provide strong resilience against
neuropathology among individuals at-risk for cognitive impairment. Among individuals
with prodromal AD, higher levels of occupational complexity do seem to provide
resilience to tau-related pathology measured with CSF markers but is not associated
with better episodic memory performance. Measuring occupational complexity with the
DOT or O*NET system seems to yield similar results, as the two systems scores are
correlated
Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health
There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimerâs Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents
List of Abbreviations ..................................................................................................................... i
List of Figures .............................................................................................................................. ii
Acknowledgements .....................................................................................................................iii
1 INTRODUCTION .....................................................................................................................1
1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3
1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4
1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4
1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6
1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8
1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8
1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10
2 PUBLICATIONS ....................................................................................................................12
2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks
and memory performance in adults .................................................................................13
2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of
human medial temporal lobe to sex hormone fluctuations ..............................................28
2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance
in the healthy female brain ...............................................................................................51
2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated
neurochemical changes across the female lifespan: implications for depression ..........65
2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in
patients with premenstrual dysphoric disorder: a case-control longitudinal neuro-
receptor ligand PET imaging study ..................................................................................82
3 SUMMARY ...........................................................................................................................100
References ..............................................................................................................................107
Supplementary Publications ...................................................................................................114
Author Contributions to Publication 1 .....................................................................................184
Author Contributions to Publication 2 .....................................................................................186
Author Contributions to Publication 3 .....................................................................................188
Author Contributions to Publication 4 .....................................................................................190
Author Contributions to Publication 5 .....................................................................................191
Declaration of Authenticity ......................................................................................................193
Curriculum Vitae ......................................................................................................................194
List of Publications ................................................................................................................195
List of Talks and Posters ......................................................................................................19
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field
2023 GREAT Day Program
SUNY Geneseoâs Seventeenth Annual GREAT Day. Geneseo Recognizing Excellence, Achievement & Talent Day is a college-wide symposium celebrating the creative and scholarly endeavors of our students. http://www.geneseo.edu/great_dayhttps://knightscholar.geneseo.edu/program-2007/1017/thumbnail.jp
Proceedings XXIII Congresso SIAMOC 2023
Il congresso annuale della SocietĂ Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto questâanno alla sua ventitreesima edizione, approda nuovamente a Roma.
Il congresso SIAMOC, come ogni anno, Ăš lâoccasione per tutti i professionisti che operano nellâambito dellâanalisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle piĂč recenti innovazioni riguardanti le procedure e le tecnologie per lâanalisi del movimento nella pratica clinica.
Il congresso SIAMOC 2023 di Roma si propone lâobiettivo di fornire ulteriore impulso ad una giĂ eccellente attivitĂ di ricerca italiana nel settore dellâanalisi del movimento e di conferirle ulteriore respiro ed impatto internazionale.
Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla societĂ . Tra questi temi anche quello dellâinserimento lavorativo di persone affette da disabilitĂ anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. VerrĂ infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per lâottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis
and treatment, especially in neurologic and neuropsychiatric diseases. Many
diseases can display multiple distinct brain phenotypes across individuals,
potentially reflecting disease subtypes that can be captured using MRI and
machine learning methods. However, biological interpretability and treatment
relevance are limited if the derived subtypes are not associated with genetic
drivers or susceptibility factors. Herein, we describe Gene-SGAN - a
multi-view, weakly-supervised deep clustering method - which dissects disease
heterogeneity by jointly considering phenotypic and genetic data, thereby
conferring genetic correlations to the disease subtypes and associated
endophenotypic signatures. We first validate the generalizability,
interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We
then demonstrate its application to real multi-site datasets from 28,858
individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes
associated with hypertension, from MRI and SNP data. Derived brain phenotypes
displayed significant differences in neuroanatomical patterns, genetic
determinants, biological and clinical biomarkers, indicating potentially
distinct underlying neuropathologic processes, genetic drivers, and
susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease
subtyping and endophenotype discovery, and is herein tested on disease-related,
genetically-driven neuroimaging phenotypes
Aerobic Exercise for the Promotion of Healthy Aging: Changes in Brain Structure Assessed with New Methods
As the proportion of older individuals in the population increases, so does the scientific concern surrounding age-related deterioration of brain tissue and related cognitive decline. One modifiable lifestyle factor of interest in the pursuit to slow or even reverse age-related brain atrophy is aerobic exercise. A number of studies have already demonstrated that aerobic exercise in older age can induce maintenance (i.e., reduction of loss) of both gray and white matter volume, particularly in the frontal regions of the brain, which are vulnerable to shrinkage in older age. Other magnetic resonance imaging (MRI)-based techniques, such as quantitative MRI and diffusion-weighted MRI, have been used to measure age-related deterioration of gray and white matter integrity in both voxel-wise analyses as well as on the latent level, but whether these negative changes can be ameliorated through exercise has yet to be shown. The current dissertation includes three papers which used a number of both established and novel MRI-based metrics to quantify changes in brain tissue integrity resulting from aging, as well as to investigate whether these changes can be ameliorated through aerobic exercise.
In Paper I (Wenger et al., 2022), we tested the reliability of quantitative MRI measures, namely longitudinal relaxation rate, effective transverse relaxation rate, proton density, and magnetization transfer saturation, by measuring them in a two-day, four-session design with repositioning in the scanner. Using the intra-class effect decomposition model, we found that magnetization transfer saturation could reliably detect individual differences, validating its use to investigate changes in brain structure longitudinally, as well as correlations with other variables of interest, such as change in cardiovascular fitness.
In Paper II (Polk et al., 2022), we tested the effects of aerobic exercise on a latent factor of gray-matter structural integrity, comprising observed measures of gray-matter volume, magnetization transfer saturation, and mean diffusivity, in regions of interest that have previously shown volumetric effects of aerobic exercise. We found that gray-matter structural integrity was maintained in frontal and midline regions, and that change in gray-matter structural integrity in the right anterior cingulate cortex was positively correlated with change in cardiovascular fitness within exercising participants. These results suggest a causal relationship between aerobic exercise, cardiovascular fitness, and gray-matter structural integrity in this region.
In Paper III (Polk et al., 2022), we tested the effects of aerobic exercise on white matter integrity, measured with both established and recently developed metrics. We were able to replicate findings from a previous study on the effects of aerobic exercise on white matter volume, and we also found change-change correlations between white matter volume and cardiovascular fitness as well as between white matter volume and performance on a test of perceptual speed. We also found unexpected exercise-induced changes in the diffusion weighted imaging-derived metrics of fractional anisotropy, mean diffusivity, fiber density, and fiber density and cross-section. Specifically, we found increases (or decreases in the case of mean diffusivity) within control participants and decreases (or increases in mean diffusivity) in exercisers. Furthermore, we found that percent change in fiber density and fiber density and cross-section correlated negatively with percent change in both cardiovascular fitness and cognitive performance. This casts doubt on the functional interpretation of these measures and suggests that the âmore is betterâ principle may not be universally applicable when investigating age-related and exercise-induced changes in white matter integrity.
In sum, this dissertation showed that regular at-home aerobic exercise, which may be more accessible for older individuals than supervised exercise, can be an effective tool to ameliorate age-related decreases in a latent measure of gray-matter structural integrity as well as white matter volume. It also illuminated potential limitations of other measures of white matter integrity in the context of aging and aerobic exercise, and calls for further research into these novel measures, especially when considering functional outcomes such as cognitive performance
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