by Kristen Thompson and Dr. Stuart Fogel
School of Psychology
University of Ottawa
136 Jean-Jacques Lussier, Vanier Hall, Room 3046
Ottawa, Ontario, Canada K1N 6N5
E-mail address: sfogel[at]uottawa.ca
This user research article summarizes the publication “Brain Activation Time-Locked to Sleep Spindles Associated With Human Cognitive Abilities“. Fang, Z., Ray, L. B., Owen, A. M., & Fogel, S. M. (2019). Frontiers in neuroscience, 13, 46. doi: 10.3389/fnins.2019.00046
Research on the functional significance of sleep spindles has shown that inter-individual differences in electrophysiological spindle characteristics are highly correlated with reasoning abilities (i.e., Fluid Intelligence), but not short-term memory, or verbal abilities (i.e., Crystallized Intelligence). Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) during sleep in humans has provided evidence that night-to-night variations in spindle-related reactivation of brain regions recruited during learning, may reflect offline memory processing. However, spindles are also very trait-like. The functional significance of inter-individual differences in brain activations that occur during spindle events is not known. Here, simultaneous EEG-fMRI recordings were used to understand the functional significance of interindividual differences in brain activations, time-locked to sleep spindles. It was found that a subset of brain-activations in regions that support Fluid Intelligence were activated during spindle events, and the extent of the brain activation was specifically related to reasoning abilities, but not short-term memory or verbal abilities. Thus, spindle-dependent activation was associated with cognitive abilities that support Fluid Intelligence. These results suggest that spindles are a physiological marker of intellectual abilities, particularly those which support problem solving, ability to employ logic and identify complex patterns.
Sleep spindles (bursts of neural oscillations between 11 and 16 Hz) are a defining feature of non-rapid eye movement (NREM) sleep (Iber, Ancoli-Israel, Chesson & Quan, 2007). The brain regions that are recruited during spindle-events have been previously identified (Laufs, Walker & Lund, 2007; Schabus, Dang-Vu, Albouy, Balteau, Boly & Carrier, 2007; Tyvaert, Levan, Grova, Dubeau & Gotman, 2008; Andrade et al., 2011; Caporro et al., 2012), and include the thalamus, temporal lobe, cingulate cortex, cortical motor areas, hippocampus, and putamen. However, the functional significance of these activations is not yet known.
Spindles appear to be trait-like; they are stable from night-to-night within individuals, but vary greatly between individuals (Silverstein & Levy, 1976). It has been suggested that spindles may be an electrophysiological marker of cognitive ability, particularly reasoning ability (i.e., Fluid Intelligence; problem solving, ability to employ logic, and identify complex patterns; Nader & Smith, 2001, 2003; Bódizs et al., 2005, 2008; Schabus et al., 2006; Fogel, Nader, Cote & Smith, 2007; Ujma et al., 2014, 2015; Fang, Sergeeva, Ray, Viczko, Owen & Fogel, 2017). However, the brain basis of this relationship is not known. There is no direct evidence linking trait-like cognitive abilities with inter-individual brain activations time-locked to sleep spindles.
Here, we sought to elucidate this relationship using simultaneous EEG-fMRI recordings during sleep. It was hypothesized that BOLD- activations time-locked to spindles would be correlated with cognitive abilities. Furthermore, consistent with previous studies, it was hypothesized that these spindle-related brain activations would be specifically related to reasoning ability, but not short-term memory (STM) or verbal abilities. This research will help uncover the functional significance of trait-like aspects of sleep spindles.
Thirty-five healthy participants who met the inclusion criteria (for details see: Fang, Ray, Owen & Fogel, 2019) were recruited to participate in the study. In order to be included, participants had to achieve at least five minutes of uninterrupted NREM sleep during the sleep session in the MRI scanner. Five participants did not meet the sleep criteria, and were not included. The final sample consisted of twenty-nine participants (mean age = 23.97, SD = 3.83, 17 female, all right handed), who attained an average sleep duration of 44 minutes in the scanner. All study procedures and methods adhered to the Declaration of Helsinki and were approved by the Western University health science research ethics board.
Cognitive Ability Assessment
Cognitive ability was assessed using the Cambridge Brain Sciences (CBS) trials. CBS is a web-based test battery, which includes 12 tasks designed to test a wide range of aspects of cognition. These various abilities cluster into 3 factors: reasoning, short-term memory, and verbal abilities. The reasoning ability factor has previously been found to be uniquely correlated with sleep spindles (Fang et al., 2017). Reasoning is best described by performance on five tasks: deductive reasoning (Cattell, 1940), spatial rotation (Silverman, Choi, Mackewn, Fisher, Moro & Olshansky, 2000), feature match (Treisman & Gelade, 1980), spatial planning (Shallice, 1982), and polygons (Folstein, Folstein & McHugh, 1975). The STM factor score is best described by performance on four tests: visuospatial working memory (Inoue & Matsuzawa, 2007), spatial span (Corsi, 1972), paired associates (Gould, Brown, Owen, Bullmore & Howard, 2006), and self-ordered search (Collins, Roberts, Dias, Everitt & Robbins, 1998). Finally, the verbal ability factor is best described by performance on three tests: verbal reasoning (Baddeley, 1968), color-word remapping (Stroop, 1935), and digit span (Wechsler, 1981).
See Figure 1 for details of the experimental paradigm. Participants completed the CBS trials battery online, after completing an initial orientation session. Participants were required to maintain a regular sleep-wake cycle (bed time between 22h00 and 24h00; wake up time between 07h00 and 09h00), and to abstain from daytime naps at least 7 days prior to participating in the study, as well as throughout the study protocol. Participants wore an Actiwatch, and kept a record of their sleep patterns and activities throughout the day while participating in the study. At least one week after the initial orientation session, participants completed a sleep session which began at 21h00 with lights out between 22h00 and 24h00, during which simultaneous EEG-fMRI was acquired while they slept in the MRI scanner.
Polysomnography and MRI Recording
EEG data was recorded using BrainVision Recorder software, version 1.x (Brain Products, Gilching, Germany). EEG was acquired using a 64-channel MR compatible EEG cap with one ECG lead (BrainCap MR, Easycap, Herrsching, Germany), and two MR-compatible 32 channel amplifiers (BrainAmp MR plus, Brain Products GmbH, Gilching, Germany) at a sampling rate of 5000 Hz, and referenced to FCz. ECG was additionally acquired from two bipolar channels using a MR-compatible 16-channel bipolar amplifier (BrainAmp ExG MR, Brain Products GmbH, Gilching, Germany). All electrode impedances were kept below 5 kΩ throughout data collection. Data were analog filtered using a band-limiter low pass filter at 500 Hz, and a high pass filter with a 10-s time constant corresponding to a frequency of 0.0159 Hz. Functional magnetic resonance imaging was performed at a 3.0T Magnetom Prisma MR imaging system (Siemens, Erlangen, Germany) using a 64-channel head coil. During the sleep session, T2∗-weighted fMRI images were acquired with a gradient echo-planar imaging (EPI) sequence that was optimized for simultaneous EEG-fMRI acquisitions (for details see: Fang, Ray, Owen & Fogel, 2019).
EEG data were corrected for gradient-induced and cardioballistic artifacts in a two-step process. First, using BrainVision Analyzer 2.x (Brain Products GmbH, Gilching, Germany), MRI gradient artifacts were removed using an adaptive average template subtraction method (Allen, Josephs & Turner, 2000), and down-sampled to 250 Hz. Second, the R-peaks in the ECG were detected using a semi-automatic process, with visual inspection and manual adjustment as needed for each and every R-peak from the QRS complex. False positives were deleted and R-peaks for false negatives were manually added. An adaptive template subtraction (Allen Polizzi, Krakow, Fish & Lemieux, 1998) was then used to remove BCG artifacts in the EEG time-locked to the R-peaks. Any residual BCG artifacts were removed using independent component analysis (ICA). A low pass filter of 60 Hz was then applied, and data was re-referenced to the average of the electrodes located at the left and right mastoids. Sleep stages were scored by an expert using standard criteria (Iber et al., 2007), and automatic spindle detection was carried out using a previously published and validated (Ray et al., 2015) method. Spindle characteristics of interest for analysis included spindle amplitude, duration, and density at electrode Cz.
Functional images were preprocessed and analyzed using SPM8 (Welcome Department of Imaging Neuroscience, London, United Kingdom) implemented in MATLAB (ver. 8.5 R2015a). For details see: Fang, Ray, Owen & Fogel, 2019. The onset time and duration of each sleep spindle was identified from the EEG, converted to TR and included in the MRI analysis as events of interest in order to identify the brain activity time-locked to the spindle events.
Consistent with previous studies (Laufs et al., 2007; Schabus et al., 2007; Tyvaert et al., 2008; Andrade et al., 2011; Caporro et al., 2012), activations time-locked to spindles (Figure 2A) were observed in the thalamus/midbrain, the bilateral striatum (putamen/globus pallidus and caudate), the medial frontal cortex, the cerebellum, and the brain stem (p < 0.05 FWE corrected at the cluster level).
A further whole brain spatial correlation analysis was conducted between brain activation maps time-locked to spindle events and the scores on the three cognitive domains from the CBS battery (reasoning, STM, and verbal abilities). Reasoning ability was significantly and uniquely correlated with spindle-related activations in the thalamus, bilateral putamen, brainstem/pons, ACC, the MCC, the paracentral lobe, the posterior cingulate cortex, the precuneus, and bilateral temporal lobe ( p < 0.001 uncorrected at the whole-brain level; and p < 0.05, FWE corrected at the cluster level; see Figure 2B-C and Figure 3). In comparison, no spindle-related activations were correlated with STM or verbal abilities (cluster-level FWE correction, p < 0.05).
Previously, sleep spindles have been found to be stable from night-to-night with great inter-individual differences, suggesting they are trait-like in nature (Silverstein & Levy, 1976; Gaillard & Blois, 1981). Furthermore, it has been suggested that spindles may serve as a biological marker of cognitive ability, particularly Fluid Intelligence, i.e., reasoning abilities (Bódizs et al., 2005; Schabus et al., 2006; Fogel et al., 2007; Fogel & Smith, 2011; Ujma et al., 2014, 2015; Fang et al., 2017). Here, we have demonstrated that reasoning abilities, but not STM or verbal abilities, were correlated with spindle-related activations in a subset of regions that support Fluid Intelligence including the thalamus, bilateral putamen, medial frontal gyrus, MCC, and precuneus. This suggests that inter-individual differences in the extent of activations in cortico–thalamic–striatal circuitry time-locked spindles are uniquely related to individual differences in reasoning ability. This research provides new insights into neural substrates that support cognitive strengths and weaknesses, and further our understanding of the functional significance of sleep spindles.
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