Rypma, Bart
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/2888
Dr. Bart Rypma's research is aimed at exploring the cognitive and neurobiological mechanisms of human memory and how those mechanisms are affected by aging and disease. He serves as Professor of Psychology and is head of the NeuroPsychometric Research Lab.. Learn more about Dr. Rypma's work on his Research Explorer page.
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Browsing Rypma, Bart by Subject "Magnetic resonance imaging"
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Item Higher-Order Cognitive Training Effects on Processing Speed-Related Neural Activity: A Randomized Trial(Elsevier) Yezhuvath, Uma S.; Aslan, Sina; Motes, Michael A.; Spence, Jeffrey S.; Rypma, Bart; Chapman, Sandra Bond; 0000 0003 5170 3614 (Chapman, SB); Motes, Michael A.; Aslan, Sina; Spence, Jeffrey S.; Rypma, Bart; Chapman, Sandra BondHigher-order cognitive training has shown to enhance performance in older adults, but the neural mechanisms underlying performance enhancement have yet to be fully disambiguated. This randomized trial examined changes in processing speed and processing speed-related neural activity in older participants (57-71years of age) who underwent cognitive training (CT, N= 12) compared with wait-listed (WLC, N= 15) or exercise-training active (AC, N= 14) controls. The cognitive training taught cognitive control functions of strategic attention, integrative reasoning, and innovation over 12weeks. All 3 groups worked through a functional magnetic resonance imaging processing speed task during 3 sessions (baseline, mid-training, and post-training). Although all groups showed faster reaction times (RTs) across sessions, the CT group showed a significant increase, and the WLC and AC groups showed significant decreases across sessions in the association between RT and BOLD signal change within the left prefrontal cortex (PFC). Thus, cognitive training led to a change in processing speed-related neural activity where faster processing speed was associated with reduced PFC activation, fitting previously identified neural efficiency profiles.Item Three‐Dimensional Lesion Phenotyping and Physiologic Characterization Inform Remyelination Ability in Multiple Sclerosis(American Society of Neuroimaging) Sivakolundu, Dinesh K.; Hansen, Madison R.; West, Kathryn L.; Wang, Yeqi; Stanley, Thomas; Wilson, Andrew; McCreary, Morgan; Turner, Monroe P.; Pinho, Marco C.; Newton, Braeden D.; Guo, Xiaohu; Rypma, Bart; Okuda, Darin T.; Sivakolundu, Dinesh K.; West, Kathryn L.; Wang, Yeqi; Stanley, Thomas; Wilson, Andrew; Turner, Monroe P.; Guo, Xiaohu; Rypma, BartBACKGROUND AND PURPOSE Multiple sclerosis (MS) clinical management is based upon lesion characterization from 2‐dimensional (2D) magnetic resonance imaging (MRI) views. Such views fail to convey the lesion‐phenotype (ie, shape and surface texture) complexity, underlying metabolic alterations, and remyelination potential. We utilized a 3‐dimensional (3D) lesion phenotyping approach coupled with imaging to study physiologic profiles within and around MS lesions and their impacts on lesion phenotypes. METHODS Lesions were identified in 3T T₂‐FLAIR images and segmented using geodesic active contouring. A calibrated fMRI sequence permitted measurement of cerebral blood flow (CBF), blood‐oxygen‐level‐dependent signal (BOLD), and cerebral metabolic rate of oxygen (CMRO₂). These metrics were measured within lesions and surrounding tissue in concentric layers exact to the 3D‐lesion shape. BOLD slope was calculated as BOLD changes from a lesion to its surrounding perimeters. White matter integrity was measured using diffusion kurtosis imaging. Associations between these metrics and 3D‐lesion phenotypes were studied. RESULTS One hundred nine lesions from 23 MS patients were analyzed. We identified a noninvasive biomarker, BOLD slope, to metabolically characterize lesions. Positive BOLD slope lesions were metabolically active with higher CMRO₂ and CBF compared to negative BOLD slope or inactive lesions. Metabolically active lesions with more intact white matter integrity had more symmetrical shapes and more complex surface textures compared to inactive lesions with less intact white matter integrity. CONCLUSION The association of lesion phenotypes with their metabolic signatures suggests the prospect for translation of such data to clinical management by providing information related to metabolic activity, lesion age, and risk for disease reactivation and self‐repair. Our findings also provide a platform for disease surveillance and outcome quantification involving myelin repair therapeutics.