Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk

Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG

Research has shown that many copy number variations (CNVs) increase the risk of neurodevelopmental disorders (e.g., autism, ADHD, schizophrenia). However, little is known about the effects of CNVs on brain development and function. Resting-state electroencephalography (EEG) is a suitable method to study the disturbances of neuronal functioning in CNVs. We aimed to determine whether there are resting-state EEG signatures that are characteristic of children with pathogenic CNVs. EEG resting-state brain activity of 109 CNV carriers (66 deletion carriers, 43 duplication carriers) aged 3 to 17 years was recorded for 4 minutes. To better account for developmental variations, EEG indices (power spectral density and functional connectivity) were corrected with a normative model estimated from 256 Healthy Brain Network controls. Results showed a decreased exponent of the aperiodic activity and a reduced alpha peak frequency in CNV carriers. Additionally, the study showed altered periodic components and connectivity in several frequency bands. Deletion and duplication carriers exhibited a similar overall pattern of deviations in spectral and connectivity measures, although the significance and effect sizes relative to the control group varied across frequency bands. Deletion and duplication carriers can be differentiated by their periodic power in the gamma band and connectivity in the low alpha band, with duplication carriers showing more disrupted alterations than deletion carriers. The distinctive alterations in spectral patterns were found to be most prominent during adolescence. The results suggest that CNV carriers show electrophysiological alterations compared to neurotypical controls, regardless of the gene dosage effect and their affected genomic region. At the same time, while duplications and deletions share common electrophysiological alterations, each exhibits distinct brain alteration signatures that reflect gene dosage-specific effects.

Exploring EEG resting state differences in autism: sparse findings from a large cohort

BACKGROUND

Autism is a complex neurodevelopmental condition, the precise neurobiological underpinnings of which remain elusive. Here, we focus on group differences in resting state EEG (rsEEG). Although many previous reports have pointed to differences between autistic and neurotypical participants in rsEEG, results have failed to replicate, sample sizes have typically been small, and only a small number of variables are reported in each study.

METHODS

Here, we combined five datasets to create a large sample of autistic and neurotypical individuals (n = 776) and extracted 726 variables from each participant's data. We computed effect sizes and split-half replication rate for group differences between autistic and neurotypical individuals for each EEG variable while accounting for age, sex and IQ. Bootstrapping analysis with different sample sizes was done to establish how effect size and replicability varied with sample size.

RESULTS

Despite the broad and exploratory approach, very few EEG measures varied with autism diagnosis, and when larger effects were found, the majority were not replicable under split-half testing. In the bootstrap analysis, smaller sample sizes were associated with larger effect sizes but lower replication rates.

LIMITATIONS

Although we extracted a comprehensive set of EEG signal components from the data, there is the possibility that measures more sensitive to group differences may exist outside the set that we tested. The combination of data from different laboratories may have obscured group differences. However, our harmonisation process was sufficient to reveal several expected maturational changes in the EEG (e.g. delta power reduction with age), providing reassurance regarding both the integrity of the data and the validity of our data-handling and analysis approaches.

CONCLUSIONS

Taken together, these data do not produce compelling evidence for a clear neurobiological signature that can be identified in autism. Instead, our results are consistent with heterogeneity in autism, and caution against studies that use autism diagnosis alone as a method to categorise complex and varied neurobiological profiles.

Linking haploinsufficiency of the autism- and schizophrenia-associated gene Cyfip1 with striatal-limbic-cortical network dysfunction and cognitive inflexibility

Impaired behavioural flexibility is a core feature of neuropsychiatric disorders and is associated with underlying dysfunction of fronto-striatal circuitry. Reduced dosage of Cyfip1 is a risk factor for neuropsychiatric disorder, as evidenced by its involvement in the 15q11.2 (BP1-BP2) copy number variant: deletion carriers are haploinsufficient for CYFIP1 and exhibit a two- to four-fold increased risk of schizophrenia, autism and/or intellectual disability. Here, we model the contributions of Cyfip1 to behavioural flexibility and related fronto-striatal neural network function using a recently developed haploinsufficient, heterozygous knockout rat line. Using multi-site local field potential (LFP) recordings during resting state, we show that Cyfip1 heterozygous rats (Cyfip1+/-) harbor disrupted network activity spanning medial prefrontal cortex, hippocampal CA1 and ventral striatum. In particular, Cyfip1+/- rats showed reduced influence of nucleus accumbens and increased dominance of prefrontal and hippocampal inputs, compared to wildtype controls. Adult Cyfip1+/- rats were able to learn a single cue-response association, yet unable to learn a conditional discrimination task that engages fronto-striatal interactions during flexible pairing of different levers and cue combinations. Together, these results implicate Cyfip1 in development or maintenance of cortico-limbic-striatal network integrity, further supporting the hypothesis that alterations in this circuitry contribute to behavioural inflexibility observed in neuropsychiatric diseases including schizophrenia and autism.

Atypical cortical networks in children at high-genetic risk of psychiatric and neurodevelopmental disorders

Although many genetic risk factors for psychiatric and neurodevelopmental disorders have been identified, the neurobiological route from genetic risk to neuropsychiatric outcome remains unclear. 22q11.2 deletion syndrome (22q11.2DS) is a copy number variant (CNV) syndrome associated with high rates of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Alterations in neural integration and cortical connectivity have been linked to the spectrum of neuropsychiatric disorders seen in 22q11.2DS and may be a mechanism by which the CNV acts to increase risk. In this study, magnetoencephalography (MEG) was used to investigate electrophysiological markers of local and global network function in 34 children with 22q11.2DS and 25 controls aged 10-17 years old. Resting-state oscillatory activity and functional connectivity across six frequency bands were compared between groups. Regression analyses were used to explore the relationships between these measures, neurodevelopmental symptoms and IQ. Children with 22q11.2DS had altered network activity and connectivity in high and low frequency bands, reflecting modified local and long-range cortical circuitry. Alpha and theta band connectivity were negatively associated with ASD symptoms while frontal high frequency (gamma band) activity was positively associated with ASD symptoms. Alpha band activity was positively associated with cognitive ability. These findings suggest that haploinsufficiency at the 22q11.2 locus impacts short and long-range cortical circuits, which could be a mechanism underlying neurodevelopmental and psychiatric vulnerability in this high-risk group.

Psychopathology in adults with copy number variants

BACKGROUND

Copy number variants (CNVs) have been associated with the risk of schizophrenia, autism and intellectual disability. However, little is known about their spectrum of psychopathology in adulthood.

METHODS

We investigated the psychiatric phenotypes of adult CNV carriers and compared probands, who were ascertained through clinical genetics services, with carriers who were not. One hundred twenty-four adult participants (age 18-76), each bearing one of 15 rare CNVs, were recruited through a variety of sources including clinical genetics services, charities for carriers of genetic variants, and online advertising. A battery of psychiatric assessments was used to determine psychopathology.

RESULTS

The frequencies of psychopathology were consistently higher for the CNV group compared to general population rates. We found particularly high rates of neurodevelopmental disorders (NDDs) (48%), mood disorders (42%), anxiety disorders (47%) and personality disorders (73%) as well as high rates of psychiatric multimorbidity (median number of diagnoses: 2 in non-probands, 3 in probands). NDDs [odds ratio (OR) = 4.67, 95% confidence interval (CI) 1.32-16.51; p = 0.017) and psychotic disorders (OR = 6.8, 95% CI 1.3-36.3; p = 0.025) occurred significantly more frequently in probands (N = 45; NDD: 39[87%]; psychosis: 8[18%]) than non-probands (N = 79; NDD: 20 [25%]; psychosis: 3[4%]). Participants also had somatic diagnoses pertaining to all organ systems, particularly conotruncal cardiac malformations (in individuals with 22q11.2 deletion syndrome specifically), musculoskeletal, immunological, and endocrine diseases.

CONCLUSIONS

Adult CNV carriers had a markedly increased rate of anxiety and personality disorders not previously reported and high rates of psychiatric multimorbidity. Our findings support in-depth psychiatric and medical assessments of carriers of CNVs and the establishment of multidisciplinary clinical services.

Neuroimaging Findings in Neurodevelopmental Copy Number Variants: Identifying Molecular Pathways to Convergent Phenotypes

Genomic copy number variants (CNVs) are associated with a high risk of neurodevelopmental disorders. A growing body of genetic studies suggests that these high-risk genetic variants converge in common molecular pathways and that common pathways also exist across clinically distinct disorders, such as schizophrenia and autism spectrum disorder. A key question is how common molecular mechanisms converge into similar clinical outcomes. We review emerging evidence for convergent cognitive and brain phenotypes across distinct CNVs. Multiple CNVs were shown to have similar effects on core sensory, cognitive, and motor traits. Emerging data from multisite neuroimaging studies have provided valuable information on how these CNVs affect brain structure and function. However, most of these studies examined one CNV at a time, making it difficult to fully understand the proportion of shared brain effects. Recent studies have started to combine neuroimaging data from multiple CNV carriers and identified similar brain effects across CNVs. Some early findings also support convergence in CNV animal models. Systems biology, through integration of multilevel data, provides new insights into convergent molecular mechanisms across genetic risk variants (e.g., altered synaptic activity). However, the link between such key molecular mechanisms and convergent psychiatric phenotypes is still unknown. To better understand this link, we need new approaches that integrate human molecular data with neuroimaging, cognitive, and animal model data, while taking into account critical developmental time points. Identifying risk mechanisms across genetic loci can elucidate the pathophysiology of neurodevelopmental disorders and identify new therapeutic targets for cross-disorder applications.

Aberrant pattern of regional cerebral blood flow in mild cognitive impairment: A meta-analysis of arterial spin labeling magnetic resonance imaging

In mild cognitive impairment (MCI), cognitive decline is associated with abnormal changes of cerebral blood flow (CBF). Arterial spin labeling magnetic resonance imaging (ASL-MRI) is an effective method for assessing regional cerebral blood flow (rCBF). However, the CBF estimated via ASL-MRI in MCI often differs between studies, and the consistency of CBF changes in MCI is unclear. In this study, 13 ASL-MRI studies with 495 MCI patients and 441 health controls were screened out from PubMed, Embase, Cochrane, Web of Science, Wanfang, and CNKI. An activation likelihood estimation (ALE) meta-analysis was performed to explore the brain regions with abnormal CBF in MCI. It showed that the decreased CBF in MCI was identified in the precuneus, inferior parietal lobule (IPL), superior occipital gyrus (SOG), middle temporal gyrus (MTG), and middle occipital gyrus (MOG), while the increased CBF in MCI was identified in the lentiform nucleus (LN) compared with healthy controls. The study characterized the abnormal pattern of regional CBF in MCI, which would promote our knowledge of MCI and might be used as a biomarker in clinic.

Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales

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MRVE measures the dynamic variability of MEG signals at a range of temporal scales.

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MRVE correlation and AEC detected robust resting state functional connectivity.

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The most robust patterns were found for f_S=75Hz MRVE correlation and beta band AEC.

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Connectivity strength negatively correlates with local MRVE at fine time scales.

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Eye movement affects resting state connectivity measurements across frequencies.

Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections. These showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band. The physiological relevance of MRVE was also investigated by examining the relationship between connectivity strength and local variability. It was found that local activity at frequencies fS≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity, as measured by fine scale MRVE correlation (fS∼ 30-150Hz) and by alpha and beta band AEC. Analysis of the EOG recordings also revealed that eye movement affected both connectivity measures. Higher levels of eye movement were associated with stronger frontal connectivity, as measured by MRVE correlation. More eye movement was also associated with reduced occipital and parietal connectivity strength for both connectivity measures, although this was not significant after correction for multiple comparisons.