Differences in Energy Balance-Related Behaviours in European Preschool Children: The ToyBox-Study

Background

The aim of the current study was to compare levels of energy balance-related behaviours (physical activity, sedentary behaviour, and dietary behaviours (more specifically water consumption, sugar-sweetened beverage consumption and unhealthy snacking)) in four- to six-year-old preschoolers from six European countries (Belgium, Bulgaria, Germany, Greece, Poland, and Spain) within the ToyBox cross-sectional study.

Methods

A sample of 4,045 preschoolers (4.77 ± 0.43 years; 52.2% boys) had valid physical activity data (steps per day), parents of 8,117 preschoolers (4.78 ± 0.46 years; 53.0% boys) completed a parental questionnaire with questions on sedentary behaviours (television viewing, computer use, and quiet play), and parents of 7,244 preschoolers (4.77 ± 0.44 years; 52.0% boys) completed a food frequency questionnaire with questions on water consumption, sugar-sweetened beverage consumption and unhealthy snacking.

Results

The highest levels of physical activity were found in Spain (12,669 steps/day on weekdays), while the lowest levels were found in Bulgaria and Greece (9,777 and 9,656 steps/day on weekdays, respectively). German preschoolers spent the least amount of time in television viewing (43.3 min/day on weekdays), while Greek preschoolers spent the most time in television viewing (88.5 min/day on weekdays). A considerable amount of time was spent in quiet play in all countries, with the highest levels in Poland (104.9 min/day on weekdays), and the lowest levels in Spain (60.4 min/day on weekdays). Belgian, German, and Polish preschoolers had the lowest intakes of water and the highest intakes of sugar-sweetened beverages. The intake of snacks was the highest in Belgian preschoolers (73.1 g/day) and the lowest in Greek preschoolers (53.3 g/day).

Conclusions

Across six European countries, differences in preschoolers’ energy balance-related behaviours were found. Future interventions should target European preschoolers’ energy balance-related behaviours simultaneously, but should apply country-specific adaptations.     

Cortical Signatures of Dyslexia and Remediation: An Intrinsic Functional Connectivity Approach

This observational, cross-sectional study investigates cortical signatures of developmental dyslexia, particularly from the perspective of behavioral remediation. We employed resting-state fMRI, and compared intrinsic functional connectivity (iFC) patterns of known reading regions (seeds) among three dyslexia groups characterized by (a) no remediation (current reading and spelling deficits), (b) partial remediation (only reading deficit remediated), and (c) full remediation (both reading and spelling deficits remediated), and a group of age- and IQ-matched typically developing children (TDC) (total N = 44, age range = 7–15 years). We observed significant group differences in iFC of two seeds located in the left posterior reading network – left intraparietal sulcus (L.IPS) and left fusiform gyrus (L.FFG). Specifically, iFC between L.IPS and left middle frontal gyrus was significantly weaker in all dyslexia groups, irrespective of remediation status/literacy competence, suggesting that persistent dysfunction in the fronto-parietal attention network characterizes dyslexia. Additionally, relative to both TDC and the no remediation group, the remediation groups exhibited stronger iFC between L.FFG and right middle occipital gyrus (R.MOG). The full remediation group also exhibited stronger negative iFC between the same L.FFG seed and right medial prefrontal cortex (R.MPFC), a core region of the default network These results suggest that behavioral remediation may be associated with compensatory changes anchored in L.FFG, which reflect atypically stronger coupling between posterior visual regions (L.FFG-R.MOG) and greater functional segregation between task-positive and task-negative regions (L.FFG-R.MPFC). These findings were bolstered by significant relationships between the strength of the identified functional connections and literacy scores. We conclude that examining iFC can reveal cortical signatures of dyslexia with particular promise for monitoring neural changes associated with behavioral remediation.     

Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome

The brain exhibits complex spatio-temporal patterns of activity. This phenomenon is governed by an interplay between the internal neural dynamics of cortical areas and their connectivity. Uncovering this complex relationship has raised much interest, both for theory and the interpretation of experimental data (e.g., fMRI recordings) using dynamical models. Here we focus on the so-called inverse problem: the inference of network parameters in a cortical model to reproduce empirically observed activity. Although it has received a lot of interest, recovering directed connectivity for large networks has been rather unsuccessful so far. The present study specifically addresses this point for a noise-diffusion network model. We develop a Lyapunov optimization that iteratively tunes the network connectivity in order to reproduce second-order moments of the node activity, or functional connectivity. We show theoretically and numerically that the use of covariances with both zero and non-zero time shifts is the key to infer directed connectivity. The first main theoretical finding is that an accurate estimation of the underlying network connectivity requires that the time shift for covariances is matched with the time constant of the dynamical system. In addition to the network connectivity, we also adjust the intrinsic noise received by each network node. The framework is applied to experimental fMRI data recorded for subjects at rest. Diffusion-weighted MRI data provide an estimate of anatomical connections, which is incorporated to constrain the cortical model. The empirical covariance structure is reproduced faithfully, especially its temporal component (i.e., time-shifted covariances) in addition to the spatial component that is usually the focus of studies. We find that the cortical interactions, referred to as effective connectivity, in the tuned model are not reciprocal. In particular, hubs are either receptors or feeders: they do not exhibit both strong incoming and outgoing connections. Our results sets a quantitative ground to explore the propagation of activity in the cortex.     

Functional Connectivity of the Caudal Anterior Cingulate Cortex Is Decreased in Autism

The anterior cingulate cortex (ACC) is frequently reported to have functionally distinct sub-regions that play key roles in different intrinsic networks. However, the contribution of the ACC, which is connected to several cortical areas and the limbic system, to autism is not clearly understood, although it may be involved in dysfunctions across several distinct but related functional domains. By comparing resting-state fMRI data from persons with autism and healthy controls, we sought to identify the abnormalities in the functional connectivity (FC) of ACC sub-regions in autism. The analyses found autism-related reductions in FC between the left caudal ACC and the right rolandic operculum, insula, postcentral gyrus, superior temporal gyrus, and the middle temporal gyrus. The FC (z-scores) between the left caudal ACC and the right insula was negatively correlated with the Stereotyped Behaviors and Restricted Interests scores of the autism group. These findings suggest that the caudal ACC is recruited selectively in the pathomechanism of autism.     

Age-Related Differences in Test-Retest Reliability in Resting-State Brain Functional Connectivity

Resting-state functional MRI (rs-fMRI) has emerged as a powerful tool for investigating brain functional connectivity (FC). Research in recent years has focused on assessing the reliability of FC across younger subjects within and between scan-sessions. Test-retest reliability in resting-state functional connectivity (RSFC) has not yet been examined in older adults. In this study, we investigated age-related differences in reliability and stability of RSFC across scans. In addition, we examined how global signal regression (GSR) affects RSFC reliability and stability. Three separate resting-state scans from 29 younger adults (18–35 yrs) and 26 older adults (55–85 yrs) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available as part of the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 92 regions of interest (ROIs) with 5 cubic mm radius, derived from the default, cingulo-opercular, fronto-parietal and sensorimotor networks, were previously defined based on a recent study. Mean time series were extracted from each of the 92 ROIs from each scan and three matrices of z-transformed correlation coefficients were created for each subject, which were then used for evaluation of multi-scan reliability and stability. The young group showed higher reliability of RSFC than the old group with GSR (p-value = 0.028) and without GSR (p-value <0.001). Both groups showed a high degree of multi-scan stability of RSFC and no significant differences were found between groups. By comparing the test-retest reliability of RSFC with and without GSR across scans, we found significantly higher proportion of reliable connections in both groups without GSR, but decreased stability. Our results suggest that aging is associated with reduced reliability of RSFC which itself is highly stable within-subject across scans for both groups, and that GSR reduces the overall reliability but increases the stability in both age groups and could potentially alter group differences of RSFC.     

Sex-Based Differences in Asthma among Preschool and School-Aged Children in Korea

The purpose of this study was to explore risk factors related to asthma prevalence among preschool and school-aged children using a representative national dataset from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2009–2011. We evaluated the demographic information, health status, household environment, socioeconomic status, and parents’ health status of 3,542 children aged 4–12 years. A sex-stratified multivariate logistic regression was used to obtain adjusted prevalence odds ratios (ORs) and 95% confidence intervals after accounting for primary sample units, stratification, and sample weights. The sex-specific asthma prevalence in the 4- to 12-year-old children was 7.39% in boys and 6.27% in girls. Boys and girls with comorbid atopic dermatitis were more likely to have asthma than those without atopic dermatitis (boys: OR = 2.20, p = 0.0071; girls: OR = 2.33, p = 0.0031). Boys and girls with ≥1 asthmatic parent were more likely to have asthma than those without asthmatic parents (boys: OR = 3.90, p = 0.0006; girls: OR = 3.65, p = 0.0138). As girls got older, the prevalence of asthma decreased (OR = 0.90, p = 0.0408). Girls residing in rural areas were 60% less likely to have asthma than those residing in urban areas (p = 0.0309). Boys with ≥5 family members were more likely to have asthma than those with ≤3 family members (OR = 2.45, p = 0.0323). The factors related to asthma prevalence may differ depending on sex in preschool and school-aged children. By understanding the characteristics of sex-based differences in asthma, individualized asthma management plans may be established clinically.     

Characteristics of Cardiointerval Fluctuations in Preschool Age Children at Various Functional States

The dynamics of psychophysiological characteristics, the functional state (FS), the galvanic skin response (GSR), and stochastic parameters of the cardiac rhythm were studied in a group of children of preschool age. Two variants of the directional associative experiment (AE) were used: the simple AE and the AE with additional motivation (award for successful solution of the AE test). The mean RR interval value averaged over the group and the standard deviation of the RR interval fluctuations were found to be virtually insensitive to the FS changes in the experiment, whereas the dynamics of the GSR and the fractal index (exponent of the power function intended to approximate the spectral density ratio in the power spectrum of RR fluctuations) significantly depend on the FS variation. Age-related and standard characteristics of ECGs of preschool age children are discussed in the context of the body's life-support system and intense intellectual activity.     

Functional Connectivity in the First Year of Life in Infants at Risk for Autism Spectrum Disorder: An EEG Study

In the field of autism research, recent work has been devoted to studying both behavioral and neural markers that may aide in early identification of autism spectrum disorder (ASD). These studies have often tested infants who have a significant family history of autism spectrum disorder, given the increased prevalence observed among such infants. In the present study we tested infants at high- and low-risk for ASD (based on having an older sibling diagnosed with the disorder or not) at 6- and 12-months-of-age. We computed intrahemispheric linear coherence between anterior and posterior sites as a measure of neural functional connectivity derived from electroencephalography while the infants were listening to speech sounds. We found that by 12-months-of-age infants at risk for ASD showed reduced functional connectivity compared to low risk infants. Moreover, by 12-months-of-age infants later diagnosed with ASD showed reduced functional connectivity, compared to both infants at low risk for the disorder and infants at high risk who were not later diagnosed with ASD. Significant differences in functional connectivity were also found between low-risk infants and high-risk infants who did not go onto develop ASD. These results demonstrate that reduced functional connectivity appears to be related to genetic vulnerability for ASD. Moreover, they provide further evidence that ASD is broadly characterized by differences in neural integration that emerge during the first year of life.     

Aberrant Whole-Brain Functional Connectivity and Intelligence Structure in Children with Primary Nocturnal Enuresis

Aim

To assess the potential relationship between intelligence structure abnormalities and whole-brain functional connectivity in children with primary nocturnal enuresis (PNE) with resting-state functional magnetic resonance imaging (fMRI) to provide insights into the association between these two seemingly unrelated conditions.

Methods

Intelligence testing and fMRI data were obtained from 133 right-handed children, including 67 PNE children (M/F, 39∶28; age, 10.5±1.2 y) and 66 age-matched healthy controls (M/F, 37∶29; age, 10.1±1.1 y). All intelligence tests were performed using the China-Wechsler Intelligence Scale for Children (C-WISC). Each subject’s full intelligence quotient (FIQ), verbal IQ (VIQ), performance IQ (PIQ), and memory/caution (M/C) factor was measured and recorded. Resting state fMRI scans were performed on a 3.0-T MR scanner and post-processed using REST software. Comparisons of z-score correlation coefficients between distinct cerebral regions were used to identify altered functional connectivity in PNE children.

Results

The PNE group had normal FIQ, VIQ, and PIQ values, indicating no significant variation from the control group. However, the M/C factor was significantly lower in the PNE group. Compared to the control group, PNE children exhibited overall lower levels of functional connectivity that were most apparent in the cerebello-thalamo-frontal pathway. The M/C factor significantly correlated with z-scores representing connectivity between Cerebellum_Crus1_L and Frontal_Mid_R.

Conclusion

PNE children exhibit intelligence structure imbalance and attention deficits. Our findings suggest that cerebello-thalamo-frontal circuit abnormalities are likely to be involved in the onset and progression of attention impairment in PNE children.     

Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study

Brain tissue changes in autism spectrum disorders seem to be rather subtle and widespread than anatomically distinct. Therefore a multimodal, whole brain imaging technique appears to be an appropriate approach to investigate whether alterations in white and gray matter integrity relate to consistent changes in functional resting state connectivity in individuals with high functioning autism (HFA). We applied diffusion tensor imaging (DTI), voxel-based morphometry (VBM) and resting state functional connectivity magnetic resonance imaging (fcMRI) to assess differences in brain structure and function between 12 individuals with HFA (mean age 35.5, SD 11.4, 9 male) and 12 healthy controls (mean age 33.3, SD 9.0, 8 male). Psychological measures of empathy and emotionality were obtained and correlated with the most significant DTI, VBM and fcMRI findings. We found three regions of convergent structural and functional differences between HFA participants and controls. The right temporo-parietal junction area and the left frontal lobe showed decreased fractional anisotropy (FA) values along with decreased functional connectivity and a trend towards decreased gray matter volume. The bilateral superior temporal gyrus displayed significantly decreased functional connectivity that was accompanied by the strongest trend of gray matter volume decrease in the temporal lobe of HFA individuals. FA decrease in the right temporo-parietal region was correlated with psychological measurements of decreased emotionality. In conclusion, our results indicate common sites of structural and functional alterations in higher order association cortex areas and may therefore provide multimodal imaging support to the long-standing hypothesis of autism as a disorder of impaired higher-order multisensory integration.     

A Model of Functional Brain Connectivity and Background Noise as a Biomarker for Cognitive Phenotypes: Application to Autism

We present an efficient approach to discriminate between typical and atypical brains from macroscopic neural dynamics recorded as magnetoencephalograms (MEG). Our approach is based on the fact that spontaneous brain activity can be accurately described with stochastic dynamics, as a multivariate Ornstein-Uhlenbeck process (mOUP). By fitting the data to a mOUP we obtain: 1) the functional connectivity matrix, corresponding to the drift operator, and 2) the traces of background stochastic activity (noise) driving the brain. We applied this method to investigate functional connectivity and background noise in juvenile patients (n = 9) with Asperger’s syndrome, a form of autism spectrum disorder (ASD), and compared them to age-matched juvenile control subjects (n = 10). Our analysis reveals significant alterations in both functional brain connectivity and background noise in ASD patients. The dominant connectivity change in ASD relative to control shows enhanced functional excitation from occipital to frontal areas along a parasagittal axis. Background noise in ASD patients is spatially correlated over wide areas, as opposed to control, where areas driven by correlated noise form smaller patches. An analysis of the spatial complexity reveals that it is significantly lower in ASD subjects. Although the detailed physiological mechanisms underlying these alterations cannot be determined from macroscopic brain recordings, we speculate that enhanced occipital-frontal excitation may result from changes in white matter density in ASD, as suggested in previous studies. We also venture that long-range spatial correlations in the background noise may result from less specificity (or more promiscuity) of thalamo-cortical projections. All the calculations involved in our analysis are highly efficient and outperform other algorithms to discriminate typical and atypical brains with a comparable level of accuracy. Altogether our results demonstrate a promising potential of our approach as an efficient biomarker for altered brain dynamics associated with a cognitive phenotype.     

Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.     

Task-switching Cost and Intrinsic Functional Connectivity in the Human Brain: Toward Understanding Individual Differences in Cognitive Flexibility

The human ability to flexibly alternate between tasks (i.e., task-switching) represents a critical component of cognitive control. Many functional magnetic resonance imaging (fMRI) studies have explored the neural basis of the task-switching. However, no study to date has examined how individual differences in intrinsic functional architecture of the human brain are related to that of the task-switching. In the present study, we took 11 task-switching relevant areas from a meta-analysis study as the regions of interests (ROIs) and estimated their intrinsic functional connectivity (iFC) with the whole brain. This procedure was repeated for 32 healthy adults based upon their fMRI scans during resting-state (rfMRI) to investigate the correlations between switching cost and the iFC strength across these participants. This analysis found that switch cost was negatively correlated with a set of iFC involved ROIs including left inferior frontal junction, bilateral superior posterior parietal cortex, left precuneus, bilateral inferior parietal lobule, right middle frontal gyrus and bilateral middle occipital gyrus. These connectivity profiles represent an intrinsic functional architecture of task-switching where the left inferior frontal junction plays a hub role in this brain-behavior association. These findings are highly reproducible in another validation independent sample and provide a novel perspective for understanding the neural basis of individual differences in task-switching behaviors reflected in the intrinsic architecture of the human brain.     

Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks

We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring–Visual-Sensorimotor-Auditory (VSA)–comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring–Parieto-Temporo-Frontal (PTF)–comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found–with correspondence analysis–that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins–coded by genes that most differentiate the rings–were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium channels SCNA3, SCNB3, SCN9A potassium channels KCNF1, KCNG1) and facilitate spontaneous transmitter release (calcium channel CACNA1H, Synaptotagmins SYT5, Complexin CPLX3, and synaptobrevin VAMP2).     

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.     

Processing of Feature Selectivity in Cortical Networks with Specific Connectivity

Although non-specific at the onset of eye opening, networks in rodent visual cortex attain a non-random structure after eye opening, with a specific bias for connections between neurons of similar preferred orientations. As orientation selectivity is already present at eye opening, it remains unclear how this specificity in network wiring contributes to feature selectivity. Using large-scale inhibition-dominated spiking networks as a model, we show that feature-specific connectivity leads to a linear amplification of feedforward tuning, consistent with recent electrophysiological single-neuron recordings in rodent neocortex. Our results show that optimal amplification is achieved at an intermediate regime of specific connectivity. In this configuration a moderate increase of pairwise correlations is observed, consistent with recent experimental findings. Furthermore, we observed that feature-specific connectivity leads to the emergence of orientation-selective reverberating activity, and entails pattern completion in network responses. Our theoretical analysis provides a mechanistic understanding of subnetworks’ responses to visual stimuli, and casts light on the regime of operation of sensory cortices in the presence of specific connectivity.     

Extracting Multiscale Pattern Information of fMRI Based Functional Brain Connectivity with Application on Classification of Autism Spectrum Disorders

We employed a multi-scale clustering methodology known as “data cloud geometry” to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both “fine” and “coarse” scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of and specificity of . Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.     

The Development of Elementary Forms of Thinking in Preschool Children

Among the new problems that society has posed to the educational system, that of developing preschool children's intellectual ability is of special importance. One of the central questions of intellectual training is developing children's thinking.     

Large-Scale,High-Resolution Multielectrode-Array Recording Depicts Functional Network Differences of Cortical and Hippocampal Cultures

Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30–80 Hz) and beta (12–30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100–1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems.