Rita Gil¹, Francisca F. Fernandes¹, and Noam Shemesh^1
1Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal

We investigated BOLD response profiles along the entire mouse visual pathway (with monocular stimulation) using an ultrafast fMRI acquisition with 50 ms temporal resolution and quantified onset, half-maximum and peak times. To achieve the spatial coverage with this temporal resolution, an oblique slice covering the entire visual pathway was tailored. The quantified onset times were the only parameter correlating with the visual pathway neural input order. Our findings highlight a potential importance for onset time quantification – requiring ultrafast fMRI acquisitions – as a signature capable of mapping the underlying activation sequence of events in distributed neural networks.

Binshi Bo¹, Mengyang Xu², Garth Thompson², and Zhifeng Liang^1
1Institute of Neuroscinece, CAS Center for Excellence in Brain Sciences and Intelligence Technology, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China, ²iHuman Institute, ShanghaiTech University, Shanghai, China

In functional brain imaging, the BOLD signal represents a mixture of CBF, CBV and the CMR_O2. “Calibrated fMRI” methods aim to measure CMR_O2 through a metabolic model, but typically require administration of gases which is not possible in many clinical settings. Here, we developed a calibrated fMRI technique called “M-Mapping” which combines CBF and R2' maps together to calculate relative CMR_O2 and showed awake mice has a greater value than anesthetized mice in a region specific manner. This method may have a great potential to compare brain metabolic activity across different resting activity levels for further clinical study.

Won Beom Jung^1,2, Hyun Seok Moon^1,2, Taeyi You^1,2, Jung Mi Lee¹, and Seong-Gi Kim^1,2
1Cener for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon-si, Gyeonggi-do, Korea, Republic of, ²Department of Biomedical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Korea, Republic of

Somatosensory system is communicated by feedforward and feedback projection each other during functional processing. Somatosensory fMRI response is attributed to these inter-regional reciprocal projections. Therefore, the separation of functional pathways in fMRI data is important to interpret fMRI data in circuit level. Here, to dissect somatosensory fMRI response, we compared CBV-weighted fMRI obtained at 15.2T under three conditions: excitation by sensory stimulation, silencing of somatosensory cortex by optogenetic stimulation, and combined excitation and silencing.

Francesca Mandino^1,2, Ling Yun Yeow², Chai Lean Teoh², Chun-Yao Lee², Renzhe Bi², Hasan Mohammad², Sejin Lee², Han Gyu Bae², Seung Hyun Baek², Hanqing Jasinda Lee³, Kim Peng Mitchell Lai³, Sangyong Jung², Fu Yu², Malini Olivo², John Gigg¹, and Joanes Grandjean^4
1Faculty of biology, medicine and health, University of Manchester, manchester, United Kingdom, ²Singapore Bioimaging Consortium, A*STAR, Singapore, Singapore, Singapore, ³Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, ⁴Department of Radiology and Nuclear Medicine & Donders Institute for Brain, Cognition, and Behaviour, Donders Institute, Radboud University Medical Centre, Nijmegen, Netherlands

Alzheimer’s disease (AD) is characterised by progressive memory loss, neurodegeneration and brain atrophy. Intra- and inter-regional connectivity across the brain is affected in AD, probably due to the aberrant accumulation of toxicity. The entorhinal cortex is a key region involved in the early stages of AD. We report synaptic connectivity increase in the 3xTg mouse model, by means of electrophysiological recordings in AD-susceptible brain regions, following stimulation of the entorhinal cortex, in vivo. Further, we demonstrate loss of functional connectivity with resting-state fMRI in AD-vulnerable brain regions, which converts into increased response during optogenetics photostimulation of the entorhinal cortex.

Xunda Wang^1,2, Alex T. L. Leong^1,2, Shawn Zheng kai Tan³, Teng Ma^1,2, Pek-Lan Khong⁴, Lee-Wei Lim³, and Ed Xuekui Wu^1,2,3,4
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, Hong Kong, ²Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong, ³School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong, ⁴Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

Spindle is one of the most critical brain oscillatory activities that has been shown to mediate sensory transmission and memory consolidation. However, where and how spindle-related activities are distributed at the systems level and their brain-wide propagation targets remain elusive. In this study, we demonstrate the first integrative view of the causal recruitment of brain-wide networks by thalamo-cortically initiated spindle-related activities in a temporal-frequency specific manner and verified its role in facilitating memory consolidation. 

Sangcheon Choi^1,2, Hang Zeng^1,2, Bharat Biswal³, Bruce R. Rosen⁴, and Xin Yu^1,4
1Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, ²Graduate Training Centre of Neuroscience, Tuebingen, Germany, ³Department of Biomedical Engineering, NJIT, Newark, NJ, United States, ⁴MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States

We applied line-scanning fMRI to investigate evoked hemodynamic responses in both laminar-specific gray matter (GM) and white matter (WM) in rats. Based on the WM-specific cross-correlation lag time to the laminar-specific fMRI signal, distinct WM hemodynamic responses were characterized across animals, showing a biphasic HRF with earlier lag times and a monophasic HRF with later lag times. Also, the lag-time dependent HRFs were detected in the subcortical area under the WM. Elucidating neurovascular coupling characteristics of distinct WM hemodynamic responses may help understand the progression of WM-related diseases, e.g. multiple sclerosis (MS) or small vessel disease (SVD).

Russell W Chan¹, Mazen Asaad², Bradley J Edelman¹, Hyun Joo Lee¹, Hillel Adesnik³, David Feinberg³, and Jin Hyung Lee^1,4,5,6
1Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States, ²Molecular and Cellular Physiology, Stanford University, Stanford, CA, United States, ³Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States, ⁴Bioengineering, Stanford University, Stanford, CA, United States, ⁵Neurosurgery, Stanford University, Stanford, CA, United States, ⁶Electrical Engineering, Stanford University, Stanford, CA, United States

The primary motor cortex (M1) consists of a stack of interconnected but distinct layers. However, knowledge of brain-wide circuit function of M1 layer-specific pathways is lacking. Here, we combined layer-specific Cre-driver mice, optogenetics, and fMRI with subsequent electrophysiological recordings to reveal distinct M1 layer-specific networks. All L2/3, L4, L5 and L6 stimulations evoked M1 fMRI responses, while only L5 and L6 evoked robust caudate putamen and ventrolateral thalamic nucleus responses. Subsequent LFP and spike recordings were in line with these fMRI results. Overall, our techniques and results could help investigate brain-wide layer-specific cortical circuit functions in development, aging and diseases.

Lindsay Walton^1,2,3, Matthew Verber⁴, Tzu-Hao Chao^1,2,3, R. Mark Wightman⁴, and Yen-Yu Ian Shih^1,2,3
1Center for Animal MRI, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ²Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ³Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ⁴Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

fMRI interpretations based on traditional neurovascular coupling ignore the possible impact of vasoactive neurotransmitters released during brain activity. The striatum has atypical neurovascular coupling, and the highest concentrations of vasoactive dopamine. We evoked dopamine release in ventral striatum, and used simultaneous BOLD-fMRI and fast-scan cyclic voltammetry (FSCV) to observe global hemodynamics and quantify local dopamine and oxygen changes, respectively. Voltammetric oxygen correlated highly with BOLD, and increased linearly with local dopamine release, such that dopamine hemodynamic response functions could be derived. This multimodality explores hemodynamics at multiple spatiotemporal scales with the additional context of neurotransmission, which will improve fMRI interpretation.

Anirban Sengupta¹, Arabinda Mishra^1,2, Feng Wang^1,2, Li Min Chen^1,2,3, and John C Gore^1,2,4,5,6
1Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, ²Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ³Psychology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Physics and Astronomy, Vanderbilt University Medical Center, Nashville, TN, United States, ⁵Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, United States, ⁶Biomedical Engineering, Vanderbilt University Medical Center, Nashville, TN, United States

The objective of this study was to investigate the presence of robust intrinsic networks inside the spinal cord of squirrel monkey and whether connectivity measures of these networks can detect injury in spinal cord. We used Independent Component Analysis of resting state fMRI data to obtain dorsal and ventral networks within the gray-matter of spinal cord. Within Horn Connectivity and Between Horn Connectivity measures were calculated based on the time course of Independent Components. A Support-Vector-Machine classifier could differentiate a spinal cord injured monkey from a control monkey using these connectivity measures with a low classification error of 6.67 %.