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106


Coarse behavioral context decoding

Alasfour, Abdulwahab; Gabriel, Paolo; Jiang, Xi; Shamie, Isaac; Melloni, Lucia; Thesen, Thomas; Dugan, Patricia; Friedman, Daniel; Doyle, Werner; Devinsky, Orin; Gonda, David; Sattar, Shifteh; Wang, Sonya; Halgren, Eric; Gilja, Vikash
OBJECTIVE:Current brain-computer interface (BCI) studies demonstrate the potential to decode neural signals obtained from structured and trial-based tasks to drive actuators with high performance within the context of these tasks. Ideally, to maximize utility, such systems will be applied to a wide range of behavioral settings or contexts. Thus, we explore the potential to augment such systems with the ability to decode abstract behavioral contextual states from neural activity. APPROACH/METHODS:To demonstrate the feasibility of such context decoding, we used electrocorticography (ECoG) and stereo-electroencephalography (sEEG) data recorded from the cortical surface and deeper brain structures, respectively, continuously across multiple days from three subjects. During this time, the subjects were engaged in a range of naturalistic behaviors in a hospital environment. Behavioral contexts were labeled manually from video and audio recordings; four states were considered: engaging in dialogue, rest, using electronics, and watching television. We decode these behaviors using a factor analysis and support vector machine (SVM) approach. MAIN RESULTS/RESULTS:We demonstrate that these general behaviors can be decoded with high accuracies of 73% for a four-class classifier for one subject and 71% and 62% for a three-class classifier for two subjects. SIGNIFICANCE/CONCLUSIONS:To our knowledge, this is the first demonstration of the potential to disambiguate abstract naturalistic behavioral contexts from neural activity recorded throughout the day from implanted electrodes. This work motivates further study of context decoding for BCI applications using continuously recorded naturalistic activity in the clinical setting.
PMID: 30523860
ISSN: 1741-2552
CID: 3642332

Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy

Sinha, Nishant; Wang, Yujiang; Dauwels, Justin; Kaiser, Marcus; Thesen, Thomas; Forsyth, Rob; Taylor, Peter Neal
Patients with idiopathic generalised epilepsy (IGE) typically have normal conventional magnetic resonance imaging (MRI), hence diagnosis based on MRI is challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are unclear and their relation to the pathomechanisms of epileptogenesis is poorly understood. In this study, we applied connectometry, an advanced quantitative neuroimaging technique for investigating localised changes in white-matter tissues in vivo. Analysing white matter structures of 32 subjects we incorporated our in vivo findings in a computational model of seizure dynamics to suggest a plausible mechanism of epileptogenesis. Patients with IGE have significant bilateral alterations in major white-matter fascicles. In the cingulum, fornix, and superior longitudinal fasciculus, tract integrity is compromised, whereas in specific parts of tracts between thalamus and the precentral gyrus, tract integrity is enhanced in patients. Combining these alterations in a logistic regression model, we computed the decision boundary that discriminated patients and controls. The computational model, informed with the findings on the tract abnormalities, specifically highlighted the importance of enhanced cortico-reticular connections along with impaired cortico-cortical connections in inducing pathological seizure-like dynamics. We emphasise taking directionality of brain connectivity into consideration towards understanding the pathological mechanisms; this is possible by combining neuroimaging and computational modelling. Our imaging evidence of structural alterations suggest the loss of cortico-cortical and enhancement of cortico-thalamic fibre integrity in IGE. We further suggest that impaired connectivity from cortical regions to the thalamic reticular nucleus offers a therapeutic target for selectively modifying the brain circuit for reversing the mechanisms leading to epileptogenesis.
PMCID:6356007
PMID: 30685702
ISSN: 2213-1582
CID: 3683302

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

Pressl, Christina; Brandner, Philip; Schaffelhofer, Stefan; Blackmon, Karen; Dugan, Patricia; Holmes, Manisha; Thesen, Thomas; Kuzniecky, Ruben; Devinsky, Orrin; Freiwald, Winrich A
There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.
PMID: 30472489
ISSN: 1872-6844
CID: 3631182

Not all predictions are equal: 'What' and 'When' predictions modulate activity in auditory cortex through different mechanisms

Auksztulewicz, Ryszard; Schwiedrzik, Caspar M; Thesen, Thomas; Doyle, Werner; Devinsky, Orrin; Nobre, Anna C; Schroeder, Charles E; Friston, Karl J; Melloni, Lucia
Employing predictions based on environmental regularities is fundamental for adaptive behaviour. While it is widely accepted that predictions across different stimulus attributes (e.g., time and content) facilitate sensory processing, it is unknown whether predictions across these attributes rely on the same neural mechanism. Here, to elucidate the neural mechanisms of predictions, we combine invasive electrophysiological recordings (human electrocorticography in 4 females and 2 males) with computational modelling while manipulating predictions about content ('what') and time ('when'). We found that 'when' predictions increased evoked activity over motor and prefrontal regions both at early (∼180 ms) and late (430-450 ms) latencies. 'What' predictability, however, increased evoked activity only over prefrontal areas late in time (420-460 ms). Beyond these dissociable influences, we found that 'what' and 'when' predictability interactively modulated the amplitude of early (165 ms) evoked responses in the superior temporal gyrus. We modelled the observed neural responses using biophysically realistic neural mass models, to better understand whether 'what' and 'when' predictions tap into similar or different neurophysiological mechanisms. Our modelling results suggest that 'what' and 'when' predictability rely on complementary neural processes: 'what' predictions increased short-term plasticity in auditory areas, while 'when' predictability increased synaptic gain in motor areas. Thus, content and temporal predictions engage complementary neural mechanisms in different regions, suggesting domain-specific prediction signalling along the cortical hierarchy. Encoding predictions through different mechanisms may endow the brain with the flexibility to efficiently signal different sources of predictions, weight them by their reliability, and allow for their encoding without mutual interference.SIGNIFICANCE STATEMENTPredictions of different stimulus features facilitate sensory processing. However, it is unclear whether predictions of different attributes rely on similar or different neural mechanisms. By combining invasive electrophysiological recordings of cortical activity with experimental manipulations of participants' predictions about content and time of acoustic events, we found that the two types of predictions had dissociable influences on cortical activity, both in terms of the regions involved and the timing of the observed effects. Further, our biophysical modelling analysis suggests that predictability of content and time rely on complementary neural processes: short-term plasticity in auditory areas and synaptic gain in motor areas, respectively. This suggests that predictions of different features are encoded with complementary neural mechanisms in different brain regions.
PMID: 30143578
ISSN: 1529-2401
CID: 3246602

Medial prefrontal cortex supports perceptual memory [Letter]

Schwiedrzik, Caspar M; Sudmann, Sandrin S; Thesen, Thomas; Wang, Xiuyuan; Groppe, David M; Mégevand, Pierre; Doyle, Werner; Mehta, Ashesh D; Devinsky, Orrin; Melloni, Lucia
Our visual environment constantly changes, yet we experience the world as a stable, unified whole. How is this stability achieved? It has been proposed that the brain preserves an implicit perceptual memory in sensory cortices [1] which stabilizes perception towards previously experienced states [2,3]. The role of higher-order areas, especially prefrontal cortex (PFC), in perceptual memory is less explored. Because PFC exhibits long neural time constants, invariance properties, and large receptive fields which may stabilize perception against time-varying inputs, it seems particularly suited to implement perceptual memory [4]. Support for this idea comes from a neuroimaging study reporting that dorsomedial PFC (dmPFC) correlates with perceptual memory [5]. But dmPFC also participates in decision making [6], so its contribution to perceptual memory could arise on a post-perceptual, decisional level [7]. To determine which role, if any, PFC plays in perceptual memory, we obtained direct intracranial recordings in six epilepsy patients while they performed sequential orientation judgements on ambiguous stimuli known to elicit perceptual memory [8]. We found that dmPFC activity in the high gamma frequency band (HGB, 70-150 Hz) correlates with perceptual memory. This effect is anatomically specific to dmPFC and functionally specific for memories of preceding percepts. Further, dmPFC appears to play a causal role, as a patient with a lesion in this area showed impaired perceptual memory. Thus, dmPFC integrates current sensory information with prior percepts, stabilizing visual experience against the perpetual variability of our surroundings.
PMID: 30253147
ISSN: 1879-0445
CID: 3314272

Time-resolved neural reinstatement and pattern separation during memory decisions in human hippocampus

Lohnas, Lynn J; Duncan, Katherine; Doyle, Werner K; Thesen, Thomas; Devinsky, Orrin; Davachi, Lila
Mnemonic decision-making has long been hypothesized to rely on hippocampal dynamics that bias memory processing toward the formation of new memories or the retrieval of old ones. Successful memory encoding may be best optimized by pattern separation, whereby two highly similar experiences can be represented by underlying neural populations in an orthogonal manner. By contrast, successful memory retrieval is thought to be supported by a recovery of the same neural pattern laid down during encoding. Here we examined how hippocampal pattern completion and separation emerge over time during memory decisions. We measured electrocorticography activity in the human hippocampus and posterior occipitotemporal cortex (OTC) while participants performed continuous recognition of items that were new, repeated (old), or highly similar to a prior item (similar). During retrieval decisions of old items, both regions exhibited significant reinstatement of multivariate high-frequency activity (HFA) associated with encoding. Further, the extent of reinstatement of encoding patterns during retrieval was correlated with the strength (HFA power) of hippocampal encoding. Evidence for encoding pattern reinstatement was also seen in OTC on trials requiring fine-grained discrimination of similar items. By contrast, hippocampal activity showed evidence for pattern separation during these trials. Together, these results underscore the critical role of the hippocampus in supporting both reinstatement of overlapping information and separation of similar events.
PMCID:6077719
PMID: 30006465
ISSN: 1091-6490
CID: 3192792

Focal Cortical Anomalies and Language Impairment in 16p11.2 Deletion and Duplication Syndrome

Blackmon, Karen; Thesen, Thomas; Green, Sophie; Ben-Avi, Emma; Wang, Xiuyuan; Fuchs, Benjamin; Kuzniecky, Ruben; Devinsky, Orrin
Individuals with copy number variants (CNV) in the 16p11.2 chromosomal region are at high risk for language disorders. We investigate whether the extent and location of focal cortical anomalies are associated with language impairment in individuals with 16p11.2 CNVs. High-resolution T1-weighted MRI scans from 30 16p11.2 deletion (16p-del), 25 16p11.2 duplication (16p-dup), and 90 noncarrier controls (NCC) were analyzed to derive personalized cortical anomaly maps through single-case cortical thickness (CT) comparison to age-matched normative samples. Focal cortical anomalies were elevated in both 16p-del and 16p-dup and their total extent was inversely correlated with Full-Scale IQ. Clusters of abnormally thick cortex were more extensive in the 16p-del group and clusters of abnormally thin cortex were more extensive in the 16p-dup group. Abnormally thick clusters were more extensive in left lateral temporal and bilateral postcentral and mesial occipital regions in 16p-del. Focal cortical anomalies in the left middle temporal region and pars opercularis (Broca's region) of children with 16-del were associated with lower scores on a comprehensive language evaluation. Results extend neuroanatomical findings in 16p11.2 syndrome to include spatially heterogenous focal cortical anomalies that appear to disrupt language ability in accordance with the functional specialization of left frontotemporal regions.
PMID: 28591836
ISSN: 1460-2199
CID: 2592152

Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure

Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin; Thesen, Thomas; Raj, Ashish
How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses.
PMID: 29454104
ISSN: 1095-9572
CID: 2990642

Parieto-frontal gyrification and working memory in healthy adults

Green, Sophie; Blackmon, Karen; Thesen, Thomas; DuBois, Jonathan; Wang, Xiuyuan; Halgren, Eric; Devinsky, Orrin
Gyrification of the cortical mantle is a dynamic process that increases with cortical surface area and decreases with age. Increased gyrification is associated with higher scores on cognitive tasks in adults; however, the degree to which this relationship is independent of cortical surface area remains undefined. This study investigates whether regional variation in gyrification is associated with domain-general and domain-specific cognition. Our hypothesis is that increased local gyrification confers a functional advantage that is independent of surface area. To quantify regional gyrification, we computed the local gyrification index (LGI) at each vertex and averaged across a bilateral parietal-frontal region associated with general intelligence and reasoning (Jung and Haier 2007). A sample of 48 healthy adults (24 males/24 females; ages 18-68 years) completed a high-resolution 3 T T1-weighted MRI and standardized administration of the Wechsler Adult Intelligence Scale (WAIS). We found a positive correlation between cortical gyrification and working memory, which remained significant after controlling for cortical surface area. Results suggest that a higher degree of local cortical folding confers a functional advantage that is independent from surface area and evident for more dynamic or "fluid" cognitive processes (i.e., working memory) rather than over-learned or "crystallized" cognitive processes.
PMID: 28290070
ISSN: 1931-7565
CID: 2489872

Author Correction: Low frequency transcranial electrical stimulation does not entrain sleep rhythms measured by human intracranial recordings [Correction]

Lafon, Belen; Henin, Simon; Huang, Yu; Friedman, Daniel; Melloni, Lucia; Thesen, Thomas; Doyle, Werner; Buzsaki, Gyorgy; Devinsky, Orrin; Parra, Lucas C; Liu, Anli
It has come to our attention that we did not specify whether the stimulation magnitudes we report in this Article are peak amplitudes or peak-to-peak. All references to intensity given in mA in the manuscript refer to peak-to-peak amplitudes, except in Fig. 2, where the model is calibrated to 1 mA peak amplitude, as stated. In the original version of the paper we incorrectly calibrated the computational models to 1 mA peak-to-peak, rather than 1 mA peak amplitude. This means that we divided by a value twice as large as we should have. The correct estimated fields are therefore twice as large as shown in the original Fig. 2 and Supplementary Figure 11. The corrected figures are now properly calibrated to 1 mA peak amplitude. Furthermore, the sentence in the first paragraph of the Results section 'Intensity ranged from 0.5 to 2.5 mA (current density 0.125-0.625 mA mA/cm2), which is stronger than in previous reports', should have read 'Intensity ranged from 0.5 to 2.5 mA peak to peak (peak current density 0.0625-0.3125 mA/cm2), which is stronger than in previous reports.' These errors do not affect any of the Article's conclusions.
PMCID:5830401
PMID: 29491347
ISSN: 2041-1723
CID: 2965562