Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

Zilio, Federico; Gómez‐Pilar, Javier; Cao, Shumei; Zhang, Jun; Zang, Di; Qi, Zengxin; Tan, James S.; Hiromi, Tanigawa; Wu, Xuehai; Fogel, Stuart; Huang, Zirui; Hohmann, Matthias R.; Fomina, Tatiana; Synofzik, Matthis; Grosse-Wentrup, Moritz; Owen, Adrian M.; Northoff, Georg

doi:10.1016/j.neuroimage.2020.117579

Cited by 81 publications

(81 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include a longer duration of delays in a delay discounting task 4 , stronger spatial response coding in the delay period during a non-match-to-goal task 63 , and modulating working memory performance during later periods, i.e., delay 9 . On the human side, recent fMRI and/or EEG studies demonstrate that the resting state’s ACW is directly related to higher-order cognition like the level of consciousness 64 , 65 , the sleep stage 21 , the sense of self 66 – 69 , and psychiatric disorders (see Box 2 for details). Tentatively, these data show that INT strongly shapes behavior, including perception and higher-order cognition like consciousness and self.…”

Section: Part I: Intrinsic Neural Timescales In Rest and Task Statesmentioning

confidence: 99%

“…Is INT engaged in either input or output processing? This was recently addressed by Zilio and colleagues 21 , who investigated the ACW in resting state EEG in subjects with physiologic, pharmacologic, and pathological alterations of consciousness. Under such conditions, input processing is known to be altered in distinct ways, i.e., progressive decrease (NREM sleep stages N1, N2, N3), isolation from external inputs but preserved capacity for processing of internal inputs (from the own body and brain) (REM sleep and ketamine), and extreme deficiency or complete absence of both external and internal inputs (unresponsive wakefulness syndrome, sevoflurane).…”

Section: Part Ii: Input Processing Through Intrinsic Neural Timescalesmentioning

confidence: 99%

“… On a whole-brain level, healthy awake subjects and subjects with motor deficits but preserved input processing (amyotrophic lateral sclerosis, locked-in syndrome) present short ACW, i.e., normal neural timescales accompanied by a balance of slow and fast frequencies, which is associated with a normal capacity of encoding inputs, while other physiological, pharmacological, and pathological conditions, e.g., sleep (N1-N2), unresponsive wakefulness syndrome and deep anesthesia (e.g., sevoflurane) show a progressive stretching of the ACW, i.e., prolonged neural timescales accompanied by a shift towards slower frequencies, which consequently lead to the abnormal prolongation of the input processing temporal window (the EEG signals and the ACW representations are taken from the datasets investigated in Zilio et al 21 ). …”

Section: Part Ii: Input Processing Through Intrinsic Neural Timescalesmentioning

confidence: 99%

See 2 more Smart Citations

The brain and its time: intrinsic neural timescales are key for input processing

et al. 2021

Self Cite

View full text Add to dashboard Cite

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs’ stochastics with the ongoing temporal statistics of the brain’s neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

show abstract

Section: Part I: Intrinsic Neural Timescales In Rest and Task Statesmentioning

confidence: 99%

Section: Part Ii: Input Processing Through Intrinsic Neural Timescalesmentioning

confidence: 99%

Section: Part Ii: Input Processing Through Intrinsic Neural Timescalesmentioning

confidence: 99%

See 1 more Smart Citation

The brain and its time: intrinsic neural timescales are key for input processing

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Intrinsic neural timescales can be measured by the ACW, which measures repeating patterns in a signal and enables testing for correlation in neural activity patterns at different points in time 4 . The ACW has been determined at the cellular level 4,5,34 as well as systems-level (using fMRI 1,3 and EEG/MEG 9,26,35 ). Therefore, we here use ACW to measure the spatial CP organization in the faster frequency range of MEG.…”

mentioning

confidence: 99%

Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization

et al. 2021

Self Cite

View full text Add to dashboard Cite

The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography, namely the core-periphery organization, remains an open issue. Using magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region’s time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex’s temporal hierarchy converges with its spatial core-periphery hierarchy.

show abstract

“…Intrinsic neural timescales can be measured by the autocorrelation window (ACW), which measures repeating patterns in a signal and enables testing for correlation in neural activity patterns at different points in time 4 . The ACW has been determined at the cellular level 4,5,34 as well as systems-level (using fMRI 1,3 and EEG/MEG 9,26,35 ). Therefore, we here use ACW to measure the spatial CP organization in the faster frequency range of MEG.…”

Section: Aims and Hypothesesmentioning

confidence: 99%

Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization

Golesorkhi

Gómez‐Pilar

Tumati

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The human cortex exhibits a temporal hierarchy during task states as evidenced by intrinsic neural timescale. How the task-based intrinsic neural timescale is shaped by resting state's spatial topography especially the recently established core-periphery hierarchy (with the default-mode network (DMN) at the core and sensory networks at the periphery) remains an open issue. Using MEG data from the Human Connectome Project (HCP), we investigated the intrinsic neural timescales by measuring the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows in various networks following core-periphery hierarchy. We demonstrate longer ACW-50 and ACW-0 in networks located at the core, i.e., DMN, frontoparietal network, and cingulum-operculum network, during both rest and task states. While networks at the periphery, i.e., auditory, visual, and somatomotor networks, exhibit shorter ACW-50 and ACW-0. Comparing both ACW scales during rest and task, i.e., rest-task difference revealed task-and network-specific effects. That is complemented by strong correlation of both ACW scales in rest with their counterpart during task states, following again the core-periphery hierarchy. Finally, we demonstrate that the longer window (ACW-0) exhibits better prediction in classifying a region's time window as belonging to either core or periphery. Overall, our findings provide fundamental insight into how the human cortex's temporal hierarchy of intrinsic neural timescales converges with spatial topography, the core-periphery hierarchy.Converging evidence shows an intrinsic temporal architecture in the brain 1,2,[11][12][13][14][15][3][4][5][6][7][8][9][10] . Lower-order sensory and higher-order cognitive regions/networks exhibit different temporal features during task states, that is, "temporal receptive fields" 13 or "temporal receptive windows" 1,2,12,[15][16][17][18][19][20][21][3][4][5][6][7][8][9]11 . Specifically, short segments like single words are processed mainly in lower-order regions like visual or auditory cortex while longer segments integrating different stimuli are processed in higher-order regions like default-mode network (DMN) and frontoparietal network (FPN) 1,2,12,15,16,[19][20][21][22][3][4][5][6][7][8][9]11 .Analogously, hierarchical organisation has also been observed on the spatial side. Converging evidence shows micro-and macro-scale hierarchical organization in the human cortex following what has been described as 'core-periphery' 8,9,[23][24][25][26][27][28] . A core-periphery (CP) architecture 29 can be characterized by a core that shares nodes with strong interconnections among each other; core-core connections ('dynamic core' or 'rich club'; 30-33 can be observed among regions included in the default-mode network (DMN), fronto-parietal network (FPN), and cingulum-operculum network (CON) 8,9,[23][24][25]28 . These networks constituting the core have been distinguished from those at the opposite end of the core-periphery gradient; that is, visual, auditory, and somatomotor n...

show abstract

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states

Cited by 81 publications

References 106 publications

The brain and its time: intrinsic neural timescales are key for input processing

The brain and its time: intrinsic neural timescales are key for input processing

Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization

Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization

Contact Info

Product

Resources

About