Browsing by Subject "Default mode network"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Advanced Meditation Alters Resting-State Brain Network Connectivity Correlating With Improved Mindfulness
    (Frontiers Media, 2021-11) Vishnubhotla, Ramana V.; Radhakrishnan, Rupa; Kveraga, Kestas; Deardorff, Rachael; Ram, Chithra; Pawale, Dhanashri; Wu, Yu-Chien; Renschler, Janelle; Subramaniam, Balachundhar; Sadhasivam, Senthilkumar; Radiology and Imaging Sciences, School of Medicine
    Purpose: The purpose of this study was to investigate the effect of an intensive 8-day Samyama meditation program on the brain functional connectivity using resting-state functional MRI (rs-fMRI). Methods: Thirteen Samyama program participants (meditators) and 4 controls underwent fMRI brain scans before and after the 8-day residential meditation program. Subjects underwent fMRI with a blood oxygen level dependent (BOLD) contrast at rest and during focused breathing. Changes in network connectivity before and after Samyama program were evaluated. In addition, validated psychological metrics were correlated with changes in functional connectivity. Results: Meditators showed significantly increased network connectivity between the salience network (SN) and default mode network (DMN) after the Samyama program (p < 0.01). Increased connectivity within the SN correlated with an improvement in self-reported mindfulness scores (p < 0.01). Conclusion: Samyama, an intensive silent meditation program, favorably increased the resting-state functional connectivity between the salience and default mode networks. During focused breath watching, meditators had lower intra-network connectivity in specific networks. Furthermore, increased intra-network connectivity correlated with improved self-reported mindfulness after Samyama.
  • Thumbnail Image
    Item
    APOE, TOMM40, and Sex Interactions on Neural Network Connectivity
    (Elsevier, 2022) Li, Tianqi; Pappas, Colleen; Le, Scott T.; Wang, Qian; Klinedinst, Brandon S.; Larsen, Brittany; Pollpeter, Amy; Lee, Ling Yi; Lutz, Mike W.; Gottschalk, William K.; Swerdlow, Russell H.; Nho, Kwangsik; Willette, Auriel A.; Radiology and Imaging Sciences, School of Medicine
    The Apolipoprotein E ε4 (APOE ε4) haplotype is the strongest genetic risk factor for late-onset Alzheimer‟s disease (AD). The Translocase of Outer Mitochondrial Membrane-40 (TOMM40) gene maintains cellular bioenergetics, which is disrupted in AD. TOMM40 rs2075650 (‘650) G vs. A carriage is consistently related to neural and cognitive outcomes, but it is unclear if and how it interacts with APOE. We examined 21 orthogonal neural networks among 8,222 middle-aged to aged participants in the UK Biobank cohort. ANOVA and multiple linear regression tested main effects and interactions with APOE and TOMM40 ‘650 genotypes, and if age and sex acted as moderators. APOE ε4 was associated with less strength in multiple networks, while ‘650 G vs. A carriage was related to more language comprehension network strength. In APOE ε4 carriers, ‘650 G-carriage led to less network strength with increasing age, while in non G-carriers this was only seen in women but not men. TOMM40 may shift what happens to network activity in aging APOE ε4 carriers depending on sex.
  • Thumbnail Image
    Item
    Effect of chemotherapy on default mode network connectivity in older women with breast cancer
    (Springer, 2022) Chen, Bihong T.; Chen, Zikuan; Patel, Sunita K.; Rockne, Russell C.; Wong, Chi Wah; Root, James C.; Saykin, Andrew J.; Ahles, Tim A.; Holodny, Andrei I.; Sun, Can-Lan; Sedrak, Mina S.; Kim, Heeyoung; Celis, Ashley; Katheria, Vani; Dale, William; Radiology and Imaging Sciences, School of Medicine
    Chemotherapy may impair cognition and contribute to accelerated aging. The purpose of this study was to assess the effects of chemotherapy on the connectivity of the default mode network (DMN) in older women with breast cancer. This prospective longitudinal study enrolled women aged ≥60 years with stage I–III breast cancer (CTx group) and matched healthy controls (HC group). Study assessments, consisting of resting-state functional MRI (rs-fMRI) and the Picture Sequence Memory (psm) test for episodic memory from the NIH Toolbox for Cognition, were obtained at baseline and within one month after the completion of chemotherapy for the CTx group and at matched intervals for the HC group. Two-sample t-test and FDR multiple comparison were used for statistical inference. Our analysis of the CTx group (N=19; 60–82 years of age, mean=66.6, SD=5.24) compared to the HC group (N=14; 60–78 years of age, mean=68.1, SD=5.69) revealed weaker DMN subnetwork connectivity in the anterior brain but stronger connectivity in the posterior brain at baseline. After chemotherapy, this pattern was reversed, with stronger anterior connectivity and weaker posterior connectivity. In addition, the meta-level functional network connectivity (FNC) among DMN subnetworks after chemotherapy was consistently weaker than the baseline FNC as seen in the couplings between anterior cingulate cortex (ACC) and retrosplenial (rSplenia) region, with ΔFNC(‘ACC’,’rSplenia’)=−0.14, t value=−2.44, 95%CI=[−0.27, −0.10], pFDR<0.05). The baseline FNC matrices of DMN subnetworks were correlated with psm scores (corr=0.58, p<0.05). Our results support DMN alterations as a potential neuroimaging biomarker for cancer-related cognitive impairment and accelerated aging.
  • Thumbnail Image
    Item
    Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features
    (MDPI, 2023-05-18) Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Meyers, Jacquelyn L.; Kinreich, Sivan; Pandey, Gayathri; Subbie-Saenz de Viteri, Stacey; Zhang, Jian; Kuang, Weipeng; Barr, Peter B.; Aliev, Fazil; Anokhin, Andrey P.; Plawecki, Martin H.; Kuperman, Samuel; Almasy, Laura; Merikangas, Alison; Brislin, Sarah J.; Bauer, Lance; Hesselbrock, Victor; Chan, Grace; Kramer, John; Lai, Dongbing; Hartz, Sarah; Bierut, Laura J.; McCutcheon, Vivia V.; Bucholz, Kathleen K.; Dick, Danielle M.; Schuckit, Marc A.; Edenberg, Howard J.; Porjesz, Bernice; Psychiatry, School of Medicine
    Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50–81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive “uplift” life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.