Simultaneous EEG-fMRI reveals neural substrates during sleep that support Fluid Intelligence

by Kristen Thompson and Dr. Stuart Fogel
School of Psychology, University of Ottawa, Ottawa, Ontario (Canada)
E-mail address: sfogel[at]


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).

Experimental Procedure

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.

Simultaneous EEG-fMRI reveals neural substrates during sleep that support Fluid Intelligence

Figure 1: Experimental protocol implemented in the present study. Participants were initially screened for sleep disorders, unusual sleep habits, health-related concerns, and MRI compatibility. Eligible participants were scheduled for an orientation session, where they received instructions pertaining to the study protocol, as well as a sleep diary, and Actiwatch. Participants completed the CBS tests at least 1 week prior to their scheduled simultaneous EEG-fMRI sleep session. The sleep session began at 21h00, with lights out occurring between 22h00-24h00. Figure credit: Fang, Ray, Owen & Fogel. (2019). Brain activation time-locked to sleep spindles associated with human cognitive abilities. Frontiers in neuroscience, 13. doi: 10.3389/fnins.2019.00046.

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).

Data Analysis

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).

Simultaneous EEG-fMRI reveals neural substrates during sleep that support Fluid Intelligence

Figure 2: Cerebral activations time-locked to sleep spindles and correlation between spindle-related activation and Reasoning abilities. (A) Activations time-locked to sleep spindles during NREM sleep. (B) Spatial correlation maps between activations time-locked to sleep spindles and Reasoning abilities. (C) Overlap between A (red) and B (green), with the conjunction of A and B shown in yellow. Figure credit: Fang, Ray, Owen & Fogel. (2019). Brain activation time-locked to sleep spindles associated with human cognitive abilities. Frontiers in neuroscience, 13. doi: 10.3389/fnins.2019.00046.

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).

Simultaneous EEG-fMRI reveals neural substrates during sleep that support Fluid Intelligence

Figure 3: Results of the semi-partial correlation between activations time-locked to spindle events and Reasoning ability. ROI analyses revealed that Reasoning abilities were correlated with activations in (A) the thalamus (partial correlation, r = 0.628, p < 0.001), (B) ACC/MCC (partial correlation, r = 0.585, p = 0.001), and (C) the bilateral putamen (partial correlation, r = 0.616, p = 0.001). Figure credit: Fang, Ray, Owen & Fogel. (2019). Brain activation time-locked to sleep spindles associated with human cognitive abilities. Frontiers in neuroscience, 13. doi: 10.3389/fnins.2019.00046.


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.


Allen, P. J., Josephs, O., & Turner, R. (2000).
A method for removing imaging artifact from continuous EEG recorded during functional MRI.
Neuroimage, 12(2), 230-239.

Allen, P. J., Polizzi, G., Krakow, K., Fish, D. R., & Lemieux, L. (1998).
Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction.
Neuroimage, 8(3), 229-239.

Andrade, K. C., Spoormaker, V. I., Dresler, M., Wehrle, R., Holsboer, F., Sämann, P. G., & Czisch, M. (2011).
Sleep spindles and hippocampal functional connectivity in human NREM sleep.
Journal of Neuroscience, 31(28), 10331-10339.

Baddeley, A. D. (1968).
A 3 min reasoning test based on grammatical transformation.
Psychonomic Science, 10(10), 341-342.

Bódizs, R., Kis, T., Lázár, A. S., Havrán, L., Rigó, P., Clemens, Z., & Halász, P. (2005).
Prediction of general mental ability based on neural oscillation measures of sleep.
Journal of Sleep Research, 14(3), 285-292.

Bódizs, R., Lázár, A., & Rigó, P. (2008).
Correlation of visuospatial memory ability with right parietal EEG spindling during sleep.
Acta Physiologica Hungarica, 95(3), 297-306.

Caporro, M., Haneef, Z., Yeh, H. J., Lenartowicz, A., Buttinelli, C., Parvizi, J., & Stern, J. M. (2012).
Functional MRI of sleep spindles and K-complexes.
Clinical Neurophysiology, 123(2), 303-309.

Cattell, R. B. (1940).
A culture-free intelligence test.
I. Journal of Educational Psychology, 31(3), 161.

Collins, P., Roberts, A. C., Dias, R., Everitt, B. J., & Robbins, T. W. (1998).
Perseveration and strategy in a novel spatial self-ordered sequencing task for nonhuman primates: effects of excitotoxic lesions and dopamine depletions of the prefrontal cortex.
Journal of Cognitive Neuroscience, 10(3), 332-354.

Corsi, P. M. (1973).
Human memory and the medial temporal region of the brain (Doctoral dissertation, ProQuest Information & Learning).

Fang, Z., Ray, L. B., Owen, A. M., & Fogel, S. M. (2019).
Brain activation time-locked to sleep spindles associated with human cognitive abilities.
Frontiers in Neuroscience, 13.

Fang, Z., Sergeeva, V., Ray, L. B., Viczko, J., Owen, A. M., & Fogel, S. M. (2017).
Sleep spindles and intellectual ability: epiphenomenon or directly related?.
Journal of Cognitive Neuroscience, 29(1), 167-182.

Fogel, S. M., & Smith, C. T. (2011).
The function of the sleep spindle: a physiological index of intelligence and a mechanism for sleep-dependent memory consolidation.
Neuroscience & Biobehavioral Reviews, 35(5), 1154-1165.

Fogel, S. M., Nader, R., Cote, K. A., & Smith, C. T. (2007).
Sleep spindles and learning potential.
Behavioral Neuroscience, 121(1), 1.

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975).
“Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.
Journal of Psychiatric Research, 12(3), 189-198.

Iber, C., & Iber, C. (2007).
The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications (Vol. 1). Westchester, IL: American Academy of Sleep Medicine.

Inoue, S., & Matsuzawa, T. (2007).
Working memory of numerals in chimpanzees.
Current Biology, 17(23), R1004-R1005.

Laufs, H., Walker, M. C., & Lund, T. E. (2007).
‘Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study’ — its limitations and an alternative approach.
Brain, 130(7), e75.

Nader, R. S., & Smith, C. T. (2001).
The relationship between stage 2 sleep spindles and intelligence.
Sleep, 24(1), A160.

Nader, R., & Smith, C. (2003).
A role for stage 2 sleep in memory processing.
Sleep and Brain Plasticity, 1, 87-99.

Ray, L., Sockeel, S., Soon, M., Bore, A., Myhr, A., Stojanoski, B., … & Fogel, S. (2015).
Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization.
Frontiers in Human Neuroscience, 9, 507.

Schabus, M., Dang-Vu, T. T., Albouy, G., Balteau, E., Boly, M., Carrier, J., … & Phillips, C. (2007).
Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep.
Proceedings of the National Academy of Sciences, 104(32), 13164-13169.

Schabus, M., Hödlmoser, K., Gruber, G., Sauter, C., Anderer, P., Klösch, G., … & Zeitlhofer, J. (2006).
Sleep spindle‐related activity in the human EEG and its relation to general cognitive and learning abilities.
European Journal of Neuroscience, 23(7), 1738-1746.

Shallice, T. (1982).
Specific impairments of planning. Philosophical Transactions of the Royal Society of London.
B, Biological Sciences, 298(1089), 199-209.

Silverman, I., Choi, J., Mackewn, A., Fisher, M., Moro, J., & Olshansky, E. (2000).
Evolved mechanisms underlying wayfinding: Further studies on the hunter-gatherer theory of spatial sex differences.
Evolution and Human Behavior, 21(3), 201-213.

Silverstein, L. D., & Levy, C. M. (1976).
The stability of the sigma sleep spindle.
Electroencephalography and Clinical Neurophysiology, 40(6), 666-670.

Stroop, J. R. (1935).
Studies of interference in serial verbal reactions.
Journal of Experimental Psychology, 18(6), 643.

Treisman, A. M., & Gelade, G. (1980).
A feature-integration theory of attention.
Cognitive Psychology, 12(1), 97-136.

Tyvaert, L., LeVan, P., Grova, C., Dubeau, F., & Gotman, J. (2008).
Effects of fluctuating physiological rhythms during prolonged EEG-fMRI studies.
Clinical Neurophysiology, 119(12), 2762-2774.

Ujma, P. P., Bódizs, R., Gombos, F., Stintzing, J., Konrad, B. N., Genzel, L., … & Dresler, M. (2015).
Nap sleep spindle correlates of intelligence.
Scientific Reports, 5, 17159.

Ujma, P. P., Konrad, B. N., Genzel, L., Bleifuss, A., Simor, P., Pótári, A., … & Dresler, M. (2014).
Sleep spindles and intelligence: evidence for a sexual dimorphism.
Journal of Neuroscience, 34(49), 16358-16368.

Wechsler D. A. (1981).
Wechsler Adult Intelligence Scale–Revised. New York, NY: Psychological Corporation.