Novelty preference assessed by eye tracking: A sensitive measure of impaired recognition memory in epilepsy.

OBJECTIVE: Epilepsy patients often report memory deficits despite normal objective testing, suggesting that available measures are insensitive or that non-mnemonic factors are involved. The Visual Paired Comparison Task (VPCT) assesses novelty preference, the tendency to fixate on novel images rather than previously viewed items, requiring recognition memory for the "old" images. As novelty preference is a sensitive measure of hippocampal-dependent memory function, we predicted impaired VPCT performance in epilepsy patients compared to healthy controls. METHODS: We assessed 26 healthy adult controls and 31 epilepsy patients (16 focal-onset, 13 generalized-onset, 2 unknown-onset) with the VPCT using delays of 2 or 30 s between encoding and recognition. Fifteen healthy controls and 17 epilepsy patients (10 focal-onset, 5 generalized-onset, 2 unknown-onset) completed the task at 2-, 5-, and 30-minute delays. Subjects also performed standard memory measures, including the Medical College of Georgia (MCG) Paragraph Test, California Verbal Learning Test-Second Edition (CVLT-II), and Brief Visual Memory Test-Revised (BVMT-R). RESULTS: The epilepsy group was high functioning, with greater estimated IQ (p = 0.041), greater years of education (p = 0.034), and higher BVMT-R scores (p = 0.024) compared to controls. Both the control group and epilepsy cohort, as well as focal- and generalized-onset subgroups, had intact novelty preference at the 2- and 30-second delays (p-values ≤ 0.001) and declined at 30 min (p-values > 0.05). Only the epilepsy patients had early declines at 2- and 5-minute delays (controls with intact novelty preference at p = 0.003 and p ≤ 0.001, respectively; epilepsy groups' p-values > 0.05). CONCLUSIONS: Memory for the "old" items decayed more rapidly in overall, focal-onset, and generalized-onset epilepsy groups. The VPCT detected deficits while standard memory measures were largely intact, suggesting that the VPCT may be a more sensitive measure of temporal lobe memory function than standard neuropsychological batteries.

Proposed mechanisms of tau: relationships to traumatic brain injury, Alzheimer's disease, and epilepsy.

Traumatic brain injury (TBI), Alzheimer's disease (AD), and epilepsy share proposed mechanisms of injury, including neuronal excitotoxicity, cascade signaling, and activation of protein biomarkers such as tau. Although tau is typically present intracellularly, in tauopathies, phosphorylated (p-) and hyper-phosphorylated (hp-) tau are released extracellularly, the latter leading to decreased neuronal stability and neurofibrillary tangles (NFTs). Tau cleavage at particular sites increases susceptibility to hyper-phosphorylation, NFT formation, and eventual cell death. The relationship between tau and inflammation, however, is unknown. In this review, we present evidence for an imbalanced endoplasmic reticulum (ER) stress response and inflammatory signaling pathways resulting in atypical p-tau, hp-tau and NFT formation. Further, we propose tau as a biomarker for neuronal injury severity in TBI, AD, and epilepsy. We present a hypothesis of tau phosphorylation as an initial acute neuroprotective response to seizures/TBI. However, if the underlying seizure pathology or TBI recurrence is not effectively treated, and the pathway becomes chronically activated, we propose a "tipping point" hypothesis that identifies a transition of tau phosphorylation from neuroprotective to injurious. We outline the role of amyloid beta (Aß) as a "last ditch effort" to revert the cell to programmed death signaling, that, when fails, transitions the mechanism from injurious to neurodegenerative. Lastly, we discuss targets along these pathways for therapeutic intervention in AD, TBI, and epilepsy.

In-scanner head motion and structural covariance networks.

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video.

INTRODUCTION: In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. METHODS: 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R2). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. RESULTS: Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. CONCLUSIONS: We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

Methylphenidate for attention problems in epilepsy patients: Safety and efficacy.

Children with attention deficit hyperactivity disorder (ADHD) have an increased risk of seizures, and children with epilepsy have an increased prevalence of ADHD. Adults with epilepsy often have varying degrees of attentional dysfunction due to multiple factors, including anti-seizure medications, frequent seizures, interictal discharges, underlying lesions, and psychiatric comorbidities. Currently, there are no approved medications for the treatment of epilepsy-related attentional dysfunction. Methylphenidate (MPH) is a stimulant, FDA-approved for the treatment of ADHD, and often used for ADHD in the setting of pediatric epilepsy. Large database and registry studies indicate safety of MPH in children with ADHD and epilepsy, with no significant effect on seizure frequency. Small single-dose and open-label studies suggest efficacy of MPH in adults with epilepsy-related attention deficits. Methylphenidate represents a possible treatment for attentional dysfunction due to epilepsy, but large, randomized, placebo-controlled, double-blinded studies are needed.