Faculty Bibliography

The two most common types of temporal lobe epilepsy are medial temporal sclerosis epilepsy (TLE-MTS) and MRI-normal temporal lobe epilepsy (TLE-no). TLE-MTS is specified by its stereotyped focus and spread pattern of neuronal damage, with pronounced neuronal loss in the hippocampus. TLE-no exhibits normal-appearing hippocampus and more widepsread neuronal loss. In both cases neuronal loss spread appears to be constrained by the white matter connections. Both varieties of epilepsy reveal pathological abnormalities in increased mean diffusivity (MD). We model MD distribution as a simple consequence of the propagation of neuronal damage. By applying this model on the structural brain connectivity network of healthy subjects we can predict at group level the mean diffusivity gray matter change in the epilepsy cohorts relative to a control group. DTI images were acquired from 10 patients with TLE-MTS, 11 patients with TLE-no, and 35 healthy subjects. Statistical validation at the group level suggests high correlation with measured neuronal loss (R = 0.56 for the TLE-MTS group and R = 0.364 for the TLE-no group). The results of this exploratory work pave the way for potential future clinical application of the proposed model on individual patients, including predicting neuronal loss spread, identification of seizure onset zones, and helping in surgical planning.

Interactions between the hippocampus and the cortex are critical for memory. Interictal epileptiform discharges (IEDs) identify epileptic brain regions and can impair memory, but the mechanisms by which they interact with physiological patterns of network activity are mostly undefined. We show in a rat model of temporal lobe epilepsy that spontaneous hippocampal IEDs correlate with impaired memory consolidation, and that they are precisely coordinated with spindle oscillations in the prefrontal cortex during nonrapid-eye-movement (NREM) sleep. This coordination surpasses the normal physiological ripple-spindle coupling and is accompanied by decreased ripple occurrence. IEDs also induce spindles during rapid-eye movement (REM) sleep and wakefulness-behavioral states that do not naturally express these oscillations-by generating a cortical 'down' state. In a pilot clinical examination of four subjects with focal epilepsy, we confirm a similar correlation of temporofrontal IEDs with spindles over anatomically restricted cortical regions. These findings imply that IEDs may impair memory via the misappropriation of physiological mechanisms for hippocampal-cortical coupling, which suggests a target for the treatment of memory impairment in epilepsy.

The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive movements may include spinning, body-rocking, or hand-flapping, amongst others. Despite the growing number of individuals affected by autism, an effective, accurate method of automatically quantifying such movements remains unavailable. This has negative implications for assessing the outcome of ASD intervention and drug studies. Here we present a novel approach to detecting autistic symptoms using the Microsoft Kinect v.2 to objectively and automatically quantify autistic body movements. The Kinect camera was used to film 12 actors performing three separate stereotypical motor movements each. Visual Gesture Builder (VGB) was implemented to analyze the skeletal structures in these recordings using a machine learning approach. In addition, movement detection was hard-coded in Matlab. Manual grading was used to confirm the validity and reliability of VGB and Matlab analysis. We found that both methods were able to detect autistic body movements with high probability. The machine learning approach yielded highest detection rates, supporting its use in automatically quantifying complex autistic behaviors with multi-dimensional input.

INTRODUCTION: Depression and memory dysfunction significantly impact the quality of life of patients with epilepsy. Current therapies for these cognitive and psychiatric comorbidities are limited. We explored the efficacy and safety of transcranial direct current stimulation (TDCS) for treating depression and memory dysfunction in patients with temporal lobe epilepsy (TLE). METHODS: Thirty-seven (37) adults with well-controlled TLE were enrolled in a double-blinded, sham-controlled, randomized, parallel-group study of 5days of fixed-dose (2mA, 20min) TDCS. Subjects were randomized to receive either real or sham TDCS, both delivered over the left dorsolateral prefrontal cortex. Patients received neuropsychological testing and a 20-minute scalp EEG at baseline immediately after the TDCS course and at 2- and 4-week follow-up. RESULTS: There was improvement in depression scores immediately after real TDCS, but not sham TDCS, as measured by changes in the Beck Depression Inventory (BDI change: -1.68 vs. 1.27, p<0.05) and NDDI-E (-0.83 vs. 0.9091, p=0.05). There was no difference between the groups at the 2- or 4-week follow-up. There was no effect on delayed or working memory performance. Transcranial direct current stimulation was well-tolerated and did not increase seizure frequency or interictal discharge frequency. Transcranial direct current stimulation induced an increase in delta frequency band power over the frontal region and delta, alpha, and theta band power in the occipital region after real stimulation compared to sham stimulation, although the difference did not reach statistical significance. DISCUSSION: This study provides evidence for the use of TDCS as a safe and well-tolerated nonpharmacologic approach to improving depressive symptoms in patients with well-controlled TLE. However, there were no changes in memory function immediately following or persisting after a stimulation course. Further studies may determine optimal stimulation parameters for maximal mood benefit.

Malformations of cortical development are found at higher rates in autism spectrum disorder (ASD) than in healthy controls on postmortem neuropathological evaluation but are more variably observed on visual review of in-vivo MRI brain scans. This may be due to the visually elusive nature of many malformations on MRI. Here, we utilize a quantitative approach to determine whether a volumetric measure of heterotopic gray matter in the white matter is elevated in people with ASD, relative to typically developing controls (TDC). Data from a primary sample of 48 children/young adults with ASD and 48 age-, and gender-matched TDCs, selected from the Autism Brain Imaging Data Exchange (ABIDE) open-access database, were analyzed to compare groups on (1) blinded review of high-resolution T1-weighted research sequences; and (2) quantitative measurement of white matter hypointensity (WMH) volume calculated from the same T1-weighted scans. Groupwise WMH volume comparisons were repeated in an independent, multi-site sample (80 ASD/80 TDC), also selected from ABIDE. Visual review resulted in equivalent proportions of imaging abnormalities in the ASD and TDC group. However, quantitative analysis revealed elevated periventricular and deep subcortical WMH volumes in ASD. This finding was replicated in the independent, multi-site sample. Periventricular WMH volume was not associated with age but was associated with greater restricted repetitive behaviors on both parent-reported and clinician-rated assessment inventories. Thus, findings demonstrate that periventricular WMH volume is elevated in ASD and associated with a higher degree of repetitive behaviors and restricted interests. Although the etiology of focal WMH clusters is unknown, the absence of age effects suggests that they may reflect a static anomaly.

Surface-based cortical thickness (CT) analyses are increasingly being used to investigate variations in brain morphology across the spectrum of brain health, from neurotypical to neuropathological. An outstanding question is whether individual differences in cortical morphology, such as regionally increased or decreased CT, are associated with domain-specific performance deficits in healthy adults. Since CT studies are correlational, they cannot establish causality between brain morphology and cognitive performance. A direct comparison with classic lesion methods is needed to determine whether the regional specificity of CT-cognition correlations is similar to that observed in patients with brain lesions. We address this question by comparing the neuroanatomical overlap of effects when 1) whole brain vertex-wise CT is tested as a correlate of performance variability on a commonly used neuropsychological test of executive function, Trailmaking Test Part B (TMT-B), in healthy adults and 2) voxel-based lesion-symptom mapping (VBLSM) is used to map lesion location to performance decrements on the same task in patients with frontal lobe lesions. We found that reduced performance on the TMT-B was associated with increased CT in bilateral prefrontal regions in healthy adults and that results spatially overlapped in the left dorsomedial prefrontal cortex with findings from the VBLSM analysis in patients with frontal brain lesions. Findings indicate that variations in the structural integrity of the left dorsomedial prefrontal lobe, ranging from individual CT differences in healthy adults to structural lesions in patients with neurological disorders, are associated with poor performance on the TMT-B. These converging results suggest that the left dorsomedial prefrontal region houses a critical region for the complex processing demands of TMT-B, which include visuomotor tracking, sequencing, and cognitive flexibility.

The interplay between the brain's function and structure has been of immense interest to the neuroscience and connectomics communities. In this work we develop a simple linear model relating the structural network and the functional network. We propose that the two networks are related by the structural network's Laplacian up to a shift. The model is simple to implement and gives accurate prediction of function's eigenvalues at the subject level and its eigenvectors at group level

Epilepsy is a neurological disorder in which patients have recurrent seizures. Seizures occur in conjunction with abnormal electrical brain activity which can be recorded by the electroencephalogram (EEG). Often, this abnormal brain activity consists of high amplitude regular spike-wave oscillations as opposed to low amplitude irregular oscillations in the non-seizure state. Active brain stimulation has been proposed as a method to terminate seizures prematurely, however, a general and widely-applicable approach to optimal stimulation protocols is still lacking. In this study we use a computational model of epileptic spike-wave dynamics to evaluate the effectiveness of a pseudospectral method to simulated seizure abatement. We incorporate brain connectivity derived from magnetic resonance imaging of a subject with idiopathic generalized epilepsy. We find that the pseudospectral method can successfully generate time-varying stimuli that abate simulated seizures, even when including heterogeneous patient specific brain connectivity. The strength of the stimulus required varies in different brain areas. Our results suggest that seizure abatement, modeled as an optimal control problem and solved with the pseudospectral method, offers an attractive approach to treatment for in vivo stimulation techniques. Further, if optimal brain stimulation protocols are to be experimentally successful, then the heterogeneity of cortical connectivity should be accounted for in the development of those protocols and thus more spatially localized solutions may be preferable.

Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many patients with histologically confirmed FCD have normal presurgical MRI studies ('MRI-negative'), making presurgical diagnosis difficult. The purpose of this study was to test whether a novel MRI postprocessing method successfully detects histopathologically verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and nonlesional vertices. Accuracy was defined by classifying contiguous vertices as "lesional" when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in vivo detection of visually elusive FCD lesions.