Intrinsic Neural Timescales in Autism Spectrum Disorder and Schizophrenia. A Replication and Direct Comparison Study

Abstract

Intrinsic neural timescales (INT) reflect the duration for which brain areas store information. A posterior – anterior hierarchy of increasingly longer INT has been revealed in both typically developed individuals (TD), as well as patients diagnosed with autism spectrum disorder (ASD) and schizophrenia (SZ), though INT are, overall, shorter in both patient groups. In the present study, we attempted to replicate previously reported group differences by comparing INT of TD to ASD and SZ. We replicated the previously reported result showing reduced INT in the left lateral occipital gyrus and the right post-central gyrus in SZ compared to TD. For the first time, we also directly compared the INT of the two patient groups and found that these same two areas show significantly reduced INT in SZ compared to ASD. In ASD, significant correlations were found between INT and their clinical and phenotypic characteristics. Our results point to the left lateral occipital gyrus and the right post-central gyrus as holding potential for further diagnostic refinement of SZ.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

“…As this 'periodicity' has not been explored for fMRI previously (to our knowledge), future work with this statistic is warranted, with an opportunity to optimize some of its parameters for fMRI data specifically. The 'ACF_timescale' statistic was also a high-performing feature, which was highly correlated to the fALFF (as has been noted in prior work [20])-potentially detecting similar phenomena to the previously reported changes to intrinsic neural timescales in SCZ [102,103] and ASD [103,104]. Some of the time-series features we evaluated were conceptually similar to these top-performing linear autocorrelation metrics but yielded null classification accuracies, demonstrating that algorithms derived from theoretically similar foundations can display quite different performance in a given application.…”

“…For example, the 'ACF_timescale' statistic distinguished ASD cases from controls while the fALFF did not (despite their high empirical correlation, as has been noted in previous work [37]), demonstrating that algorithms derived from theoretically similar foundations can perform differently in a given application. Interestingly, the strong performance of this timescale feature might be related to previous work showing changes to intrinsic neural timescales in SCZ [100,101] and ASD [101,102]. The nonlinear intra-regional features we examined (such as the time reversibility statistic 'trev') did enable us to distinguish cases from controls in any disorder, consistent with the view that rs-fMRI BOLD dynamics (which are noisy and sparsely sampled in time) are well approximated by a linear stochastic process such that methods aiming to capture more complex (e.g., nonlinear) dynamical structures may not be beneficial at this timescale, as has recently been proposed [103,104].…”

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

“…Intrinsic (neural) timescales (INT) reflect the time window of integration of a neuron, neuronal population, or brain region at rest. They are measured from the autocorrelation of neurophysiological signals using a range of recording methods-e.g., single-neuron recordings (Murray et al, 2014), calcium imaging (Pinto et al, 2020), electroencephalography (EEG) (Watanabe et al, 2019), resting-state functional magnetic resonance imaging (rs-fMRI) (Watanabe et al, 2019)-and are relevant to basic neural organizational structure (Murray et al, 2014), cognitive function (Cavanagh et al, 2020;Zeraati et al, 2023), and neuropsychiatric illness (Uscătescu et al, 2023;Watanabe et al, 2019;Wengler et al, 2020). INT, particularly as derived from rs-fMRI, are consequently garnering greater attention.…”

“…In terms of organizational principles of the brain, a hierarchy of timescales has been observed in mice (Pinto et al, 2020), non-human primates (Manea et al, 2022;Murray et al, 2014;Spitmaan et al, 2020), and humans (Hasson et al, 2015;Hasson et al, 2008;Honey et al, 2012;Lerner et al, 2011;Raut et al, 2020;Stephens et al, 2013;Wengler et al, 2020). Finally, INT alterations have been found across several neuropsychiatric disorders, including psychosis (Uscătescu et al, 2023;Uscătescu et al, 2021;Wengler et al, 2020), autism (Uscătescu et al, 2023;Watanabe et al, 2019), Parkinson's disease (Wei et al, 2023), epilepsy (Wang et al, 2021), obsessive-compulsive disorder (Xu et al, 2023), and Alzheimer's disease (Murai et al, 2023;Zhang et al, 2023).…”