Research

Current Research Projects and Sample Publications 

1. Neuroimaging Indicators for Cognitive Impairment

Brain mechanisms such as changes in synaptic connectivity underlying alterations in cognition are detectable before behavioral measures of cognition and clinical diagnosis. In the past decade, the Aging Brain and Cognition laboratory has developed reliable fMRI connectivity [a] and electrophysiological biomarkers (network EEG [b] or cognitive ERP [e]) measured during resting or visual cognition tasks. These biomarkers allow for the differentiation between cognitively normal adults. They can also be used to distinguish patients with Alzheimer's disease or MCI from cognitively normal older adults [c]. The same techniques have also been employed to identify subjects with TBI [d] and malingered neurocognitive deficit. At present, these methods are being used in research to characterize patients with Posttraumatic Stress Disorder (PTSD) and cancer patients treated with chemotherapy.

1. Jiang Y, Huang H, Abner E, Broster LS, Jicha GA, Schmitt FA, Kryscio R, Andersen A, Powell D, Van Eldik L, Gold BT, Nelson PT, Smith C, Ding M (2016). Alzheimer's Biomarkers are correlated with brain connectivity in older adults differentially during resting and task states. Front Aging Neurosci. 8:15. PMID: 26903858 PMCID: PMC4744860

2. McBride JC, Zhao X, Munro NB, Jicha GA, Schmitt FA, Kryscio RJ, Smith CD, Jiang Y (2015). Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease. Neuroimage Clin. 7:258-65. PMID: 25610788 PMCID: PMC4385710

3. Broster LS, Li J, Smith CD, Jicha GA, Schmitt FA, Jiang Y (2013). Repeated retrieval during working memory is sensitive to amnestic mild cognitive impairment. J Clin Exp Neuropsychol.  35:946-59. PMID: 24074205 PMCID: PMC3884808

4. Vagnini VL, Berry DT, Clark JA, Jiang Y (2008). New measures to detect malingered neurocognitive deficit: applying reaction time and event-related potentials. J Clin Exp Neuropsychol. 30(7):766-76. PMID: 18608662 PMCID: PMC3649037

5. Li, J, Broster, L, Jicha, G, Munro, N, Schmitt, F, Abner, E, Kryscio, R, Smith, C, & Jiang, Y (2016). A cognitive electrophysiological signature differentiates amnestic mild cognitive impairment from normal aging, Alzheimer's Research & Therapy, in press. 

2. Brain Mechanisms in Human Visual Memory and Cognitive Aging

We have advanced the understanding of brain mechanisms involved in two types of short-term memory in humans, explicit working memory and implicit repetition learning memory. Jiang et al. (2000) used fMRI to characterize and first report the existence of these two complementary forms of memory [2d]. After the ABC laboratory was established at the University of Kentucky, we extended this study and reported new evidence on the temporal dynamics of the neural mechanisms underlying working memory [2a] and repetition learning [2c]. We also identified that during cognitive aging, brain responses are altered during working memory retrieval, even though memory accuracy remains high [2b]. Please see a review on cognitive performance and neuroimaging and genetic approaches in [2e].

1. Guo C, Lawson AL, Zhang Q, Jiang Y. Brain potentials distinguish new and studied objects during working memory. Hum Brain Mapp. 29(4):441-52. PMID: 17497630 PMCID: PMC3665269

2. Lawson AL, Guo C, Jiang Y (2007). Age effects on brain activity during repetition priming of targets and distracters. Neuropsychologia. 45(6):1223-31. PMID: 17140610 PMCID: PMC1850388

3. Guo C, Lawson AL, Jiang Y (2007). Distinct neural mechanisms for repetition effects of visual objects. Neuroscience. 149(4):747-59. Erratum in: Neuroscience. 2008 May 15;153(3):871-4. PMID: 17949920 PMCID: PMC2203616

4. Jiang Y, Haxby JV, Martin A, Ungerleider LG, Parasuraman R (2000). Complementary neural mechanisms for tracking items in human working memory. Science. 287(5453):643-6. PMID: 10649996

5. Parasuraman, R & Jiang, Y (2012).   Individual differences in cognition, affect, and performance: Behavioral, neuroimaging, and molecular genetic approaches, NeuroImage, 59 (1), p70-82. 

3. Visual Processing of Motion-in-depth / Rotating Three-dimensional Objects, and implications for recovery from brain injury

How the brain processes visual luminance, motion, depth, 3-D object shape defined by movement [e] in different parts of visual cortex, and combines this information into a coherent perception of a rotating 3-D object is still not completely understood. By conducting experiments using visual psychophysics, fMRI imaging, magnetoencephalography (MEG), and EEG imaging approaches, results support the idea that combining different visual information on a single object in a 3-D space occurs through synchronized oscillating brain activity in different brain regions [b]. Using simultaneous fMRI and eye-tracking recordings, we identified distinct brain networks underlying motion perception and visual memory [a,b,c]. These findings have shown promising clinical application for assessing brain injury recovery of athletes after sports injuries [d].

1. Ding J, Powell D, Jiang Y (2009). Dissociable frontal controls during visible and memory-guided eye-tracking of moving targets. Hum Brain Mapp. 30(11):3541-52. PMID: 19434603 PMCID: PMC2767402

2. Jiang Y, Boehler CN, Nönnig N, Düzel E, Hopf JM, Heinze HJ, Schoenfeld MA (2008). Binding 3-D object perception in the human visual cortex. J Cogn Neurosci. 20(4):553-62. PMID: 18052779 PMCID: PMC3658156

3. Jiang Y, Ding JH, Gold BT, Powell D (2008). The hemispheric asymmetries in tracking occluded moving targets with the mind’s eye: Simultaneous event-related fMRI and eye-movement recording. Brain Imaging and Behavior. 2(4): 300-8.

4. Cripps, A, Livingston, SC, Jiang, Y, Mattacola, C, Van Meter, E, Kitzman, P, McKeon, P (2015). Viscuo-Motor processing impairments following concussion in athletes, J Athl Enhancement, 4:3, 1000197, 1-6.

5. Jiang Y, Pantle AJ, Mark LS (1998). Visual inertia of rotating 3-D objects. Perception & Psychophysics, 60(2):275-86. PMID: 9529911 

4. Individual Differences in Neural Mechanisms of Personality, Affect, and Risky Social Decision-making

In recent years, the ABC lab has investigated how individual differences in genetics and personality modulate behavior and underlying brain responses, e.g. lower-functioning dopaminergic activity (e.g. dopamine D2 receptor) puts individuals at risk for violence because it motivates them to experience aggression's rewarding qualities [a]. We report new findings that sensation seeking, boredom, or math anxiety modulate brain responses in affective processing[c], decision-making [d], repeated perceptual experience [e], and cognitive task performance [b]. Much of this work was initiated by students and collaborators and it has significantly broadened our research horizon.

1. Chester DS, DeWall CN, Derefinko KJ, Estus S, Lynam DR, Peters JR, Jiang Y (2016). Looking for reward in all the wrong places: dopamine receptor gene polymorphisms indirectly affect aggression through sensation-seeking. Soc Neurosci. 11(5):487-94. doi: 10.1080/17470919.2015.1119191. Epub 2015 Dec 7; PMID: 26592425; PMC4981173

2. Lawson AL, Liu X, Joseph J, Vagnini VL, Kelly TH, Jiang Y (2012). Sensation seeking predicts brain responses in the old-new task: converging multimodal neuroimaging evidence. Int J Psychophysiol. 84(3):260-9. PMID: 22484516 PMCID: PMC3367102

3. Jones WJ, Childers TL, Jiang Y (2012). The shopping brain: math anxiety modulates brain responses to buying decisions. Biol Psychol. 89(1):201-13. PMID: 22027087

4. Xu, P, Gu, R, Broster, L, Wu, R, Van Dam, N, Jiang, Y, Fan, J, & Luo, Y (2013). Neural basis of emotional decision making in trait anxiety, Journal of Neuroscience, 33 (47): 18641-18653. PMID 24259585, PMC3834062.

5. Jiang Y, Lianekhammy J, Lawson A, Guo C, Lynam D, Joseph JE, Gold BT, Kelly TH (2009). Brain responses to repeated visual experience among low and high sensation seekers: role of boredom susceptibility. Psychiatry Res. 173(2):100-6. PMID: 19560906 PMCID: PMC2774088