CLAUDIA DA COSTA LEITE
Projetos de Pesquisa
Unidades Organizacionais
Departamento de Radiologia, Faculdade de Medicina - Docente
LIM/44 - Laboratório de Ressonância Magnética em Neurorradiologia, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/44 - Laboratório de Ressonância Magnética em Neurorradiologia, Hospital das Clínicas, Faculdade de Medicina - Líder
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conferenceObject Distinction of MS-cognitive profiles is associated with fatigue, anxiety and depression status and present different brain atrophy patterns(2022) RIMKUS, C. de Medeiros; AVOLIO, I. M. Bello; NUCCI, M. P.; PEREIRA, S. L. Apostolos; CALLEGARO, D.; SCHOONHEIM, M. M.; BARKHOF, F.; LEITE, C. da Costa- Phenotyping Superagers Using Resting-State fMRI(2023) GODOY, L. L. de; STUDART-NETO, A.; PAULA, D. R. de; GREEN, N.; HALDER, A.; ARANTES, P.; CHAIM, K. T.; MORAES, N. C.; YASSUDA, M. S.; NITRINI, R.; DRESLER, M.; LEITE, C. da Costa; PANOVSKA-GRIFFITHS, J.; SODDU, A.; BISDAS, S.BACKGROUND AND PURPOSE: Superagers are defined as older adults with episodic memory performance similar or superior to that in middle-aged adults. This study aimed to investigate the key differences in discriminative networks and their main nodes between superagers and cognitively average elderly controls. In addition, we sought to explore differences in sensitivity in detecting these functional activities across the networks at 3T and 7T MR imaging fields. MATERIALS AND METHODS: Fifty-five subjects 80 years of age or older were screened using a detailed neuropsychological protocol, and 31 participants, comprising 14 superagers and 17 cognitively average elderly controls, were included for analysis. Participants underwent resting-state-fMRI at 3T and 7T MR imaging. A prediction classification algorithm using a penalized regression model on the measurements of the network was used to calculate the probabilities of a healthy older adult being a superager. Additionally, ORs quantified the influence of each node across preselected networks. RESULTS: The key networks that differentiated superagers and elderly controls were the default mode, salience, and language networks. The most discriminative nodes (ORs > 1) in superagers encompassed areas in the precuneus posterior cingulate cortex, prefrontal cortex, temporoparietal junction, temporal pole, extrastriate superior cortex, and insula. The prediction classification model for being a superager showed better performance using the 7T compared with 3T resting-state-fMRI data set. CONCLUSIONS: Our findings suggest that the functional connectivity in the default mode, salience, and language networks can provide potential imaging biomarkers for predicting superagers. The 7T field holds promise for the most appropriate study setting to accurately detect the functional connectivity patterns in superagers.
- Gray matter networks and cognitive impairment in multiple sclerosis(2019) RIMKUS, Carolina M.; SCHOONHEIM, Menno M.; STEENWIJK, Martijn D.; VRENKEN, Hugo; EIJLERS, Anand J. C.; KILLESTEIN, Joep; WATTJES, Mike P.; LEITE, Claudia C.; BARKHOF, Frederik; TIJMS, Betty M.Background: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS). Objective: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS. Methods: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level. Results: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p<0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values. Conclusion: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.
conferenceObject Cognitive decline in multiple sclerosis is associated with structural network disruption - a single-subject network approach(2016) RIMKUS, C. M.; SCHOONHEIM, M. M.; STEENWIJK, M. D.; WATTJES, M.; KILLESTEIN, J.; LEITE, C. C.; BARKHOF, F.; TIJMS, B. M.- Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review(2019) MOURA, Luciana M.; LUCCAS, Rafael; PAIVA, Joselisa P. Q. de; AMARO JR., Edson; LEEMANS, Alexander; LEITE, Claudia da C.; OTADUY, Maria C. G.; CONFORTO, Adriana B.Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.