Faculty Bibliography

We present a new approach to extracting low-dimensional neural trajectories that summarize the electrocorticographic (ECoG) signals recorded with high-channel-count electrode arrays implanted subdurally. In our approach, Hidden-Markov Factor Analysis (HMFA), a finite set of factor analyzers are used to model the relationship between the high-dimensional ECoG neural space and a low-dimensional latent neural space; the factor analyzers at different time points are in turn linked together with a hidden Markov model. The recorded ECoG signals were band-pass filtered such that our analysis was focused on a sub-band (76-100Hz) of high gamma. HMFA affords the quantization of the ECoG neural space and dimensionality reduction in a common probabilistic space. We applied this method to the ECoG recordings of 2 subjects who responded with button presses to audiovisual stimuli in an experimental task. Using a goodness-of-fit metric that measures how well the ECoG activity of each electrode can be predicted by all the other electrodes, we found that HMFA performed best when compared with Gaussian-Process Factor Analysis (GPFA) and other related spatiotemporal modeling techniques. In contradistinction to HMFA, GPFA and the other techniques integrate temporal smoothing with dimensionality reduction. We believe that this method will provide a powerful tool for relating high-channel-count ECoG signals to the perception and behavior of subjects.

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.

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.

Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common cause in adults with treatment-resistant epilepsy. Surgical resection of the lesion is the most effective treatment to stop seizures. Technical advances in MRI have revolutionized the diagnosis of FCD, leading to high success rates for resective surgery. However, 45% of histologically confirmed FCD patients have normal MRIs (MRI-negative). Without a visible lesion, the success rate of surgery drops from 66% to 29%. In this work, we cast the problem of detecting potential FCD lesions using MRI scans of MRI-negative patients in an image segmentation framework based on hierarchical conditional random fields (HCRF). We use surface based morphometry to model the cortical surface as a two-dimensional surface which is then segmented at multiple scales to extract superpixels of different sizes. Each superpixel is assigned an outlier score by comparing it to a control population. The lesion is detected by fusing the outlier probabilities across multiple scales using a tree-structured HCRF. The proposed method achieves a higher detection rate, with superior recall and precision on a sample of twenty MRI-negative FCD patients as compared to a baseline across four morphological features and their combinations.

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.

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.

Focal cortical dysplasia (FCD) is a malformation of cortical development that is associated with high rates of cognitive morbidity. However, the degree to which specific irregularities of dysplastic tissue directly impact cognition remains unknown. This study investigates the relationship between blurring of the cortical gray and white matter boundary on magnetic resonance imaging (MRI) and global cognitive abilities in FCD. Gray-white blurring (GWB) is quantified by sampling the non-normalized T1 image intensity contrast above and below the gray and white matter interface along the cortical mantle. Spherical averaging is used to compare resulting GWB for patients with histopathologically verified FCD with matched controls. Whole-brain correlational analyses are used to investigate the relationship between blurring and general cognitive abilities, controlling for epilepsy duration. Results show that cognitive performance is reduced in patients with FCD relative to controls. Patients show increased GWB in bilateral temporal, parietal, and frontal regions. Furthermore, increased GWB in these regions is linearly related to decreased cognition and mediates group differences in cognitive performance. These findings demonstrate that GWB is a marker of reduced cognitive efficiency in FCD that can potentially be used to probe general and domain-specific cognitive functions in other neurological disorders.

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.

OBJECTIVE: For patients with medically intractable focal epilepsy, the benefit of epilepsy surgery must be weighed against the risk of cognitive decline. Clinical factors such as age and presurgical cognitive level partially predict cognitive outcome; yet, little is known about the role of cross-hemispheric white matter pathways in supporting postsurgical recovery of cognitive function. The purpose of this study is to determine whether the presurgical corpus callosum (CC) midsagittal area is associated with pre- to postsurgical change following epilepsy surgery. METHODS: In this observational study, we retrospectively identified 24 adult patients from an epilepsy resection series who completed preoperative high-resolution T1 -weighted magnetic resonance imaging (MRI) scans, as well as pre- and postsurgical neuropsychological testing. The total area and seven subregional areas of the CC were measured on the midsagittal MRI slice using an automated method. Standardized indices of auditory-verbal working memory and delayed memory were used to probe cognitive change from pre- to postsurgery. CC total and subregional areas were regressed on memory-change scores, after controlling for overall brain volume, age, presurgical memory scores, and duration of epilepsy. RESULTS: Patients had significantly reduced CC area relative to healthy controls. We found a positive relationship between CC area and change in working memory, but not delayed memory; specifically, the larger the CC, the greater the postsurgical improvement in working memory (beta = 0.523; p = 0.009). Effects were strongest in posterior CC subregions. There was no relationship between CC area and presurgical memory scores. SIGNIFICANCE: Findings indicate that larger CC area, measured presurgically, is related to improvement in working memory abilities following epilepsy surgery. This suggests that transcallosal pathways may play an important, yet little understood, role in postsurgical recovery of cognitive functions.

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.