⭐7T fMRI 工作坊報名時間延長至2025/11/21(五)!
7T fMRI workshop registration deadline extended to Friday, November 21, 2025!
報名與註冊 Registration and Submission
年會專題講座 Keynote Address
時間 Date: 2026/01/24(Sat) 09:00 - 09:50,10:00 - 10:50
地點 Venue: 國立陽明交通大學(陽明校區)活動中心第三會議室
3rd Meeting Room, Auditorium and Activity Center, National Yang Ming Chiao Tung University (Yang Ming Campus)
Fa-Hsuan Lin
Professor
University of Toronto
Intracranial stereoelectroencephalography (SEEG) has the unprecedented sensitivity and specificity in recording human neurophysiology. However, analyzing SEEG data across patients is challenging when pooling results into a common atlas due to disparate electrode implantation across individuals. To mitigate this challenge, we propose the distributed source modeling of SEEG based on the individual’s brain anatomy. We demonstrate how this method is used to estimate the spatial distribution of intracranial event-related potential sources and high broadband gamma activity, a putative correlate of local neural firing. We then extend SEEG distributed source modeling to estimate neural activity across cortical depths, thereby disentangling feedforward and feedback information processing in the sensory cortices. Using auditory stimuli, we found that neural current estimates were stronger in the deep and superficial depths before and after 500 ms since stimulus onset, respectively. Neural current estimates in the auditory cortex under cross-modal audiovisual stimulation were less variable across cortical depths at early (150 ms) and intermediate (250 ms) peaks than under the unimodal audio stimulation, while variability across cortical depths was larger after 500 ms. Together, these results demonstrate the potential of SEEG for achieving high spatiotemporal-resolution imaging of neural activity, complementing other imaging modalities.
Nathaniel Daw
Huo Professor in Computational and Theoretical Neuroscience
Princeton University
The brain must often make decisions in tasks -- like mazes, social situations, or investment -- where candidate actions are separated from their consequences by many steps of space and time. A central computational problem in decision making is spanning these gaps to work out the long-term consequences of candidate actions. I review recent experimental and theoretical work aimed at understanding the mechanisms by which the brain solves this problem. Our understanding of this parallels the development of approaches to this problem in artificial intelligence: following early enthusiasm about planning by exhaustive search, both computer scientists and neuroscientists have come to understand the importance of judiciously pretraining and adapting one's computations to future needs. This offers a nerw perspective on a range of issues such as habits and automaticity in the healthy brain, but also suggests candidate mechanisms that may underlie dysfunctions such as compulsion, rumination, and avoidance.
7T fMRI工作坊講座資訊
Workshop on 7T fMRI
時間 Date: 2026/01/23(Fri) 13:30 - 16:30
地點 Venue: 國立陽明交通大學(陽明校區)活動中心第一會議室
1st Meeting Room, Auditorium and Activity Center, National Yang Ming Chiao Tung University (Yang Ming Campus)
Jonathan Polimeni
Associate Professor of Radiology
Stanford University
All fMRI techniques in use today measure brain function only indirectly, by tracking the changes in blood flow, volume and oxygenation that accompany neuronal activity, and this has often been viewed as the fundamental limitation of the technique. However, recent evidence from invasive in-vivo microscopy studies has shown that the brain's smallest blood vessels respond far more precisely, in space and in time, to neuronal activity than previously believed. This insight suggests that the “biological resolution” of fMRI is intrinsically high, and, with sufficiently high imaging resolution, it should be possible to extract more meaningful neuronally specific information from fMRI—if we can understand how brain vascular anatomy and physiology shape the hemodynamics that generate the fMRI signals.
In this presentation, I will describe ongoing efforts to improve the neuronal specificity of fMRI and pose the question: How far can we go with fMRI? The limits of fMRI spatial and temporal resolution are actively being investigated using advanced imaging technologies. While high-resolution human fMRI studies are increasingly operating at the boundaries of what is achievable, a key challenge is that the vascular architecture of the brain reflects its structure and function across spatial scales. Both classic and modern vascular anatomy studies have shown how the macro-vascular geometry is coupled with the tissue geometry, including the gray matter folds and the white matter tracts, while the micro-vascular density closely follows borders of subcortical nuclei, cortical areas and cortical layers. I will present evidence that both the large- and small-scale vascular anatomy strongly influence patterns of fMRI activation and describe strategies for how to account for this.
As examples of the intrinsically high biological resolution of fMRI, I will present results showing cortical columnar and laminar imaging, and new directions in the emerging field of ”fast fMRI” that show how the BOLD response can track surprisingly fast neural dynamics. Lastly, I will share our recent progress towards building bottom-up biophysical models of the fMRI signals based on realistic vascular anatomy and dynamics that provide insights into the interrelationship between hemodynamics and neural activity. Overall, many lessons can be learned through a deeper understanding of brain vascular anatomy and physiology, which can both shed light on the brain's functional organization and help neuroscientists more accurately interpret the fMRI signals in terms of the underlying neural activity.
Fa-Hsuan Lin
Professor
University of Toronto
7T fMRI enables the resolution of brain activity across cortical depths to understand feedforward and feedback dynamics. The relationship between these hemodynamic signals and neural activity is less well explored. In this talk, we present results correlating 7T fMRI in healthy individuals with invasive electrophysiological recordings from epilepsy patients to examine layer-dependent coupling between neuronal activity and fMRI during passive music listening. Specifically, Layer-specific fMRI responses were modeled using neuronal oscillation envelopes elicited by the same naturalistic stimuli. From deep toward superficial layers, the relationship between oscillatory power and fMRI responses systematically changed: alpha/beta activity (8-30 Hz) was increasingly associated with negative fMRI responses, while gamma band (>30 Hz) oscillations showed increasingly positive associations. The envelope of broadband high-frequency activity (>70 Hz) showed the strongest link with fMRI signals in the intermediate layers. This "feedforward type" dominance of intermediate layers was also clearly present in the fMRI analysis using the acoustical envelope itself. Our findings reveal a spectrolaminar organization of neurovascular coupling in the human auditory cortex.
報名時間 Registration Deadline:
投稿時間:即日起至2025/11/14(五) 23:59
For Submission: from now on until 2025/11/14(Fri.)23:59
出席報名截止:2026/01/09(五)23:59
For Attendence: from now on until 2026/01/09(Fri.)23:59
活動時間 Date:
2026/01/24(六)Sat.
08:30~08:45 報到 Registration,08:45 會議開始 Conference begins
地點 Venue:
國立陽明交通大學(陽明校區) 活動中心第三會議室。
3rd Meeting Room, Auditorium and Activity Center,
National Yang Ming Chiao Tung University (Yang Ming Campus)
時間 Date:
2026/01/23(五)Fri.
10:00-10:30 報到 Registration,10:30 競賽開始 Competition begins
地點 Venue:
國立陽明交通大學(陽明校區)活動中心第一會議室
1st Meeting Room, Auditorium and Activity Center,
National Yang Ming Chiao Tung University (Yang Ming Campus)
報名時間 Registration Deadline:
即日起至2025/11/21(五)23:59
From now on until 2025/11/21(Fri.)F23:59
活動時間 Date:
2026/01/23(五)Fri.
13:00-13:30 報到 Registration,13:30 活動開始 Begins
地點 Venue:
國立陽明交通大學(陽明校區)活動中心第一會議室
1st Meeting Room, Auditorium and Activity Center,
National Yang Ming Chiao Tung University (Yang Ming Campus)
講者 Speaker:
Prof. Jonathan Polimeni (U. Stanford)
Prof. Fa-Hsuan Lin (University of Toronto)
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