Independent component analysis (ICA) has been widely used for electroencephalography (EEG) analyses. However, ICA performance relies on several crucial assumptions about the data. Here, we focus on the granularity of data rank, i.e., the number of linearly independent EEG channels. When the data are rank-full (i.e., all channels are independent), ICA produces as many independent components (ICs) as the number of input channels (rank-full decomposition). However, when the input data are rank-deficient, as is the case with bridged or interpolated electrodes, ICA produces the same number of ICs as the data rank (forced rank deficiency decomposition), introducing undesired ghost ICs and indicating a bug in ICA. We demonstrated that the ghost ICs have white noise properties, in both time and frequency domains, while maintaining surprisingly typical scalp topographies, and can therefore be easily missed by EEG researchers and affect findings in unknown ways. This problem occurs when the minimum eigenvalue λmin of the input data is smaller than a certain threshold, leading to matrix inversion failure as if the rank-deficient inversion was forced, even if the data rank is cleanly deficient by one. We defined this problem as the effective rank deficiency. Using sound file mixing simulations, we first demonstrated the effective rank deficiency problem and determined that the critical threshold for λmin is 10⁻⁷ in the given situation. Second, we used empirical EEG data to show how two preprocessing stages, re-referencing to average without including the initial reference and non-linear electrode interpolation, caused this forced rank deficiency problem. Finally, we showed that the effective rank deficiency problem can be solved by using the identified threshold (λmin = 10⁻⁷) and the correct re-referencing procedure described herein. The former ensures the achievement of effective rank-full decomposition by properly reducing the input data rank, and the latter allows avoidance of a widely practiced incorrect re-referencing approach. Based on the current literature, we discuss the ambiguous status of the initial reference electrode when re-referencing. We have made our data and code available to facilitate the implementation of our recommendations by the EEG community.

Fragile X Syndrome (FXS) is a monogenetic form of intellectual disability and autism in which well-established knockout (KO) animal models point to neuronal hyperexcitability and abnormal gamma-frequency physiology as a basis for key disorder features. Translating these findings into patients may identify tractable treatment targets. Using source modeling of resting-state electroencephalography data, we report findings in FXS, including 1) increases in localized gamma activity, 2) pervasive changes of theta/alpha activity, indicative of disrupted thalamocortical modulation coupled with elevated gamma power, 3) stepwise moderation of low and high-frequency abnormalities based on female sex, and 4) relationship of this physiology to intellectual disability and neuropsychiatric symptoms. Our observations extend findings inFmr1^−/−KO mice to patients with FXS and raise a key role for disrupted thalamocortical modulation in local hyperexcitability. This systems-level mechanism has received limited preclinical attention but has implications for understanding fundamental disease mechanisms.

The pathophysiology of Chronic Tic Disorders (CTDs), including Tourette Syndrome, remains poorly understood. The goal of this study was to compare neural activity and connectivity during a voluntary movement (VM) paradigm that involved cued eye blinks among children with and without CTDs. Using the precise temporal resolution of electroencephalography (EEG), we used the timing and location of cortical source resolved spectral power activation and connectivity to map component processes such as visual attention, cue detection, blink regulation and response monitoring. We hypothesized that neural activation and connectivity during the cued eye blink paradigm would be significantly different in regions typically associated with effortful control of eye blinks, such as frontal, premotor, parietal, and occipital cortices between children with and without CTD. Participants were 40 children (23 with CTD, 17 age-matched Healthy Control [HC]), between the ages of 8-12 (mean age = 9.5) years old. All participants underwent phenotypic assessment including diagnostic interviews, behavior rating scales and 128-channel EEG recording. Upon presentation of a cue every 3 s, children were instructed to make an exaggerated blink. Behaviorally, the groups did not differ in blink number, latency, or ERP amplitude. Within source resolved clusters located in left dorsolateral prefrontal cortex, posterior cingulate, and supplemental motor area, children with CTD exhibited higher gamma band spectral power relative to controls. In addition, significant diagnostic group differences in theta, alpha, and beta band power in inferior parietal cortex emerged. Spectral power differences were significantly associated with clinical characteristics such as tic severity and premonitory urge strength. After calculating dipole density for 76 anatomical regions, the CTD and HC groups had 70% overlap of top regions with the highest dipole density, suggesting that similar cortical networks were used across groups to carry out the VM. The CTD group exhibited significant information flow increase and dysregulation relative to the HC group, particularly from occipital to frontal regions. Children with CTD exhibit abnormally high levels of neural activation and dysregulated connectivity among networks used for regulation and effortful control of voluntary eye blinks.