Over 30 years of functional imaging studies have demonstrated that the human brain operates as a complex and interconnected system, with distinct functional networks and long-range coordination of neural activity. Yet, how our brains coordinate our behavior from moment to moment, permitting us to think, talk, and move at the same time, has been almost impossible to decode (Chapter 1).
The invasive, long-term, and often multi-regional iEEG monitoring utilized for epilepsy surgery evaluation presents a valuable opportunity for studying brain-wide dynamic neural activity in behaving human subjects. In this study, we analyzed over 93 hours of iEEG recordings along with simultaneously acquired video recordings from 10 patients with drug-resistant focal epilepsy syndromes, who underwent invasive iEEG with broadly distributed bilateral depth electrodes for clinical evaluation.
Initially, we explored the dynamic connectivity patterns quantified from band-limited neural activities using metrics from previous literature in a subset of subjects. These metrics can characterize long-range connectivity across brain regions and reveal variations over time. They have shown success in identifying state differences using controlled task presentations and trial-averaged data. However, we found that replicating this success with naturalistic, complex behaviors in our subjects is challenging. Although they demonstrate differences across wake and sleep states, they are less sensitive in differentiating more complicated and subtle state transitions during wakefulness. In addition, patterns identified from individual frequency bands exhibit patient-to-patient differences, making it difficult to generalize results across frequency bands and subjects. (Chapter 2).
Inspired by clinical electrocortical stimulation mapping studies, which seek to identify critical brain sites for language and motor function, and the frequency gradient observed from human scalp and intracranial EEG recordings, we developed a new approach to meet the requirements for real-time analysis and frequency band selection. It is worth mentioning that detecting state transitions in naturalistic behavior requires analyzing raw EEG during individual transitions. We refer to this as "real-time analysis," to distinguish it from formal task performance and trial-averaging techniques. Rather than representing data as time-varying signals within specific frequency bands, we incorporated all frequencies (2-55 Hz) into our analysis by calculating the power spectral density (PSD) at each electrode. This analysis confirmed that the human brain’s neural activity PSD is heterogenous, exhibiting a distinct topography with bilateral symmetry, consistent with prior resting-state MEG and iEEG studies. However, investigating the variability of each region’s PSD over time (within a 2-second moving window), we discovered the tendency of individual electrode channel to switch back and forth between 2 distinct power spectral densities (PSDs, 2-55Hz) (Chapter 3).
We further recognized that this ‘spectral switching’ occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that could be obscured in the analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation, hand movements, and eyes open versus closed, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. These network-behavior relationships were stable over multiple days but were altered during sleep, suggesting state-dependent plasticity of brain-wide network organization (Chapter 4).
Our results provide robust evidence for the presence of multiple synchronous neuronal networks across the human brain. The real-time PSD switching dynamics of these networks provide physiologically interpretable read-outs, demonstrating the parallel engagement of multiple brain regions in a range of concurrent naturalistic behaviors (Chapter 5).
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/wr67-m305 |
| Date | January 2024 |
| Creators | Zhu, Hongkun |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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