Data Availability statement
All spectral analyses and connectivity analyses were performed using
Fieldtrip toolbox available at https://www.fieldtriptoolbox.org/. Figure
data and codes used here are available at
https://github.com/supratimray/TLSAEEGProjectPrograms under the
connectivityProject folder. The raw data that support the findings of
this study are openly available in the OSF repository at
https://osf.io/ebryn/
Introduction
Neural coordination referred as functional connectivity (FC)
(Fingelkurts et al. , 2005), refers to the statistical
relationship between spatially distant neurophysiological events which
is generally considered to be mediated by neuronal assemblies (Mcintosh,
1999). FC can be studied at various spatial and temporal scales, using
brain signals such as fMRI, electro/magneto-encephalogram (EEG/MEG) or
neuronal firing activity, and is quantified using a variety of
techniques such as coherence (Srinivasan et al. , 2007), imaginary
part of coherence (Nolte et al. , 2004), phase locking value (PLV;
(Lachaux et al. , 2000)), pairwise phase consistency (PPC; (Vincket al. , 2010)) and so on. Many studies have reported changes in
FC in different frequency bands with cognitive tasks such as working
memory visual discrimination (Lachaux et al. , 1998), emotional
face processing (Li et al. , 2015), fixation tasks (Murty et
al. , 2018), pain perception tasks (Alba et al. , 2022), motor
learning tasks (Heitger et al. , 2012), aging and with brain
disorders (Ishii et al. , 2017). For example, reduction in alpha
(8-12 Hz) FC was reported with healthy aging in resting state EEG
(Moezzi et al. , 2019), while gamma (30-55 Hz) FC reduction was
reported in autism (Safar et al. , 2020), and in Alzheimer’s
Disease (AD) and schizophrenia (Uhlhaas & Singer, 2006). Interestingly,
some studies have also reported an increase in gamma FC in subjects with
mild-cognitive-impairment (MCI) (Rodinskaia et al. , 2022) and AD
(Stam et al. , 2009).
Majority of EEG FC studies that describe the effect of aging and
cognitive disorder are during resting state, where many factors can
alter the data, such as specific instructions, cognitive state before
recording, caffeine intake, or random episodic spontaneous thoughts (van
Diessen et al. , 2015). Having a stimulus dependent paradigm
restricts subject specific variations. Such responses are termed as
either evoked or induced, based on their trial-wise relationship with
stimulus onset (Tallon-Baudry & Bertrand, 1999). During periods of
spontaneous activity, while alpha (Gómez et al. , 2013; Scallyet al. , 2018) and beta rhythms are prominent, with distinct bumps
in the power spectral density (PSD) of brain signals, there is rarely
any periodic activity in the gamma range. On the other hand,
presentation of certain visual stimuli such as long bars and large
gratings produce a distinct “narrow-band gamma” – with a bandwidth of
~20 Hz and centre frequency between 30-70 Hz (Grayet al. , 1989) – which has a clear bump in the PSD and whose
power and centre frequency depend on stimulus properties (Jia et
al. , 2013; Murty et al. , 2018). This stimulus-induced narrowband
gamma is also distinct from “broadband gamma”, which is typically
associated with increases in firing rate (see, for example, (Ray &
Maunsell, 2011)). While there are many studies where the effect of
FC/power is compared in MCI vs healthy controls ((Berendse et
al. , 2000; Rodinskaia et al. , 2022); see Discussion for more
references), these were studied mainly during resting state, and
therefore did not produce narrowband gamma. Recent studies have shown
that such narrow-band gamma oscillations can be induced in human EEG/MEG
by presentation of stationary cartesian or moving annular gratings
(Orekhova et al. , 2015; Murty et al. , 2018; Pantaziset al. , 2018), whose power changes with age (Murty et al. ,
2020) and mental disorders such as MCI/AD (Murty et al. , 2021).
However, whether aging or mental
disorders change stimulus-induced gamma FC, to our knowledge, is not
well studied.
Further, while both aging and cognitive disorders generally reduce the
power of brain oscillations, the effect of these two factors on FC could
be different. For example, Sullivan and colleagues (2019) reported that
healthy aging and MCI produce distinct changes in fMRI FC (measured
using bivariate-correlation) across different networks in the brain.
Another study (Finnigan & Robertson, 2011) reported altered
relationship between alpha (8-12 Hz) and theta (4-8 Hz) oscillations
with healthy aging and pathological aging. Finding robust connectivity
measures that distinguish aging versus mental disorder
could help in developing better
biomarkers that are more sensitive to the onset of cognitive disorders
compared to power-based biomarkers.
The main objective of this study is to test how stimulus-induced gamma
FC varies with healthy aging and in MCI subjects compared to their
healthy controls. We recorded EEG data from healthy (N=218) and MCI
(N=11) subjects while they maintained fixation on large achromatic
gratings presented on a monitor (same dataset as the one used in (Murtyet al. , 2020, 2021)). Large gratings were shown to induce two
stimulus-induced gamma oscillations, termed “slow” (20-34 Hz) and
“fast” (35-65 Hz) (Murty et al. , 2018, 2020, 2021). Further,
these large stimuli produced a prominent suppression of the alpha power.
Since the stimuli produced prominent changes in alpha, slow gamma and
fast gamma bands, we estimated FC in these three bands using PPC, which
is an unbiased estimator of the squared PLV (see (Vinck et al. ,
2010) for details). We first
analysed the effect of aging on visual stimulus-induced FC by splitting
subjects into middle-aged (50-64 yrs) and elderly (>64 yrs)
groups, as done in previous studies (Murty et al. , 2020, 2021).
Since FC could potentially be dependent on power along with aging, we
performed a power-matching procedure (see detailed below) to control for
power dependence. We also tested FC dependency on power and age using a
robust linear regression model. Finally, we compared the FC for MCI
subjects versus their age and gender matched controls.