Spatiotemporally distributed frontotemporal networks for sentence reading.

Reading a sentence entails integrating the meanings of individual words to infer more complex, higher-order meaning. This highly rapid and complex human behavior is known to engage the inferior frontal gyrus (IFG) and middle temporal gyrus (MTG) in the language-dominant hemisphere, yet whether there are distinct contributions of these regions to sentence reading is still unclear. To probe these neural spatiotemporal dynamics, we used direct intracranial recordings to measure neural activity while reading sentences, meaning-deficient Jabberwocky sentences, and lists of words or pseudowords. We isolated two functionally and spatiotemporally distinct frontotemporal networks, each sensitive to distinct aspects of word and sentence composition. The first distributed network engages the IFG and MTG, with IFG activity preceding MTG. Activity in this network ramps up over the duration of a sentence and is reduced or absent during Jabberwocky and word lists, implying its role in the derivation of sentence-level meaning. The second network engages the superior temporal gyrus and the IFG, with temporal responses leading those in frontal lobe, and shows greater activation for each word in a list than those in sentences, suggesting that sentential context enables greater efficiency in the lexical and/or phonological processing of individual words. These adjacent, yet spatiotemporally dissociable neural mechanisms for word- and sentence-level processes shed light on the richly layered semantic networks that enable us to fluently read. These results imply distributed, dynamic computation across the frontotemporal language network rather than a clear dichotomy between the contributions of frontal and temporal structures.

A Spatiotemporal Map of Reading Aloud.

Reading words aloud is a fundamental aspect of literacy. The rapid rate at which multiple distributed neural substrates are engaged in this process can only be probed via techniques with high spatiotemporal resolution. We probed this with direct intracranial recordings covering most of the left hemisphere in 46 humans (26 male, 20 female) as they read aloud regular, exception and pseudo-words. We used this to create a spatiotemporal map of word processing and to derive how broadband γ activity varies with multiple word attributes critical to reading speed: lexicality, word frequency, and orthographic neighborhood. We found that lexicality is encoded earliest in mid-fusiform (mFus) cortex, and precentral sulcus, and is represented reliably enough to allow single-trial lexicality decoding. Word frequency is first represented in mFus and later in the inferior frontal gyrus (IFG) and inferior parietal sulcus (IPS), while orthographic neighborhood sensitivity resides solely in IPS. We thus isolate the neural correlates of the distributed reading network involving mFus, IFG, IPS, precentral sulcus, and motor cortex and provide direct evidence for parallel processes via the lexical route from mFus to IFG, and the sublexical route from IPS and precentral sulcus to anterior IFG.SIGNIFICANCE STATEMENT Reading aloud depends on multiple complex cerebral computations: mapping from a written letter string on a page to a sequence of spoken sound representations. Here, we used direct intracranial recordings in a large cohort while they read aloud known and novel words, to track, across space and time, the progression of neural representations of behaviorally relevant factors that govern reading speed. We find, concordant with cognitive models of reading, that known and novel words are differentially processed through a lexical route, sensitive to frequency of occurrence of known words in natural language, and a sublexical route, performing letter-by-letter construction of novel words.

Critical role of the ventral temporal lobe in naming.

OBJECTIVE: Lexical retrieval deficits are characteristic of a variety of different neurological disorders. However, the exact substrates responsible for this are not known. We studied a large cohort of patients undergoing surgery in the dominant temporal lobe for medically intractable epilepsy (n = 95) to localize brain regions that were associated with anomia. METHODS: We performed a multivariate voxel-based lesion-symptom mapping analysis to correlate surgical lesions within the temporal lobe with changes in naming ability. Additionally, we used a surface-based mixed-effects multilevel analysis to estimate group-level broadband gamma activity during naming across a subset of patients with electrocorticographic recordings and integrated these results with lesion-deficit findings. RESULTS: We observed that ventral temporal regions, centered around the middle fusiform gyrus, were significantly associated with a decline in naming. Furthermore, we found that the ventral aspect of temporal lobectomies was linearly correlated to a decline in naming, with a clinically significant decline occurring once the resection extended 6 cm from the anterior tip of the temporal lobe on the ventral surface. On electrocorticography, the majority of these cortical regions were functionally active following visual processing. These loci coincide with the sites of susceptibility artifacts during echoplanar imaging, which may explain why this region has been previously underappreciated as the locus responsible for postoperative naming deficits. SIGNIFICANCE: Taken together, these data highlight the crucial contribution of the ventral temporal cortex in naming and its important role in the pathophysiology of anomia following temporal lobe resections. As such, surgical strategies should attempt to preserve this region to mitigate postoperative language deficits.

Imaging the effective networks associated with cortical function through intracranial high-frequency stimulation.

Direct electrical stimulation (DES) is considered to be the gold standard for mapping cortical function. A careful mapping of the eloquent cortex is key to successful resective or ablative surgeries, with a minimal postoperative deficit, for treatment of drug-resistant epilepsy. There is accumulating evidence suggesting that not only local, but also remote activations play an equally important role in evoking clinical effects. By introducing a new intracranial stimulation paradigm and signal analysis methodology allowing to disambiguate EEG responses from stimulation artifacts we highlight the spatial extent of the networks associated with clinical effects. Our study includes 26 patients that underwent stereoelectroencephalographic investigations for drug-resistant epilepsy, having 337 depth electrodes with 4,351 contacts sampling most brain structures. The routine high-frequency electrical stimulation protocol for eloquent cortex mapping was altered in a subtle way, by alternating the polarity of the biphasic pulses in a train, causing the splitting the spectral lines of the artifactual components, exposing the underlying tissue response. By performing a frequency-domain analysis of the EEG responses during DES we were able to capture remote activations and highlight the effect's network. By using standard intersubject averaging and a fine granularity HCP-MMP parcellation, we were able to create local and distant connectivity maps for 614 stimulations evoking specific clinical effects. The clinical value of such maps is not only for a better understanding of the extent of the effects' networks guiding the invasive exploration, but also for understanding the spatial patterns of seizure propagation given the timeline of the seizure semiology.

Cingulate cortex function and multi-modal connectivity mapped using intracranial stimulation.

The cingulate cortex is part of the limbic system. Its function and connectivity are organized in a rostro-caudal and ventral-dorsal manner which was addressed by various other studies using rather coarse cortical parcellations. In this study, we aim at describing its function and connectivity using invasive recordings from patients explored for focal drug-resistant epilepsy. We included patients that underwent stereo-electroencephalographic recordings using intracranial electrodes in the University Emergency Hospital Bucharest between 2012 and 2019. We reviewed all high frequency stimulations (50 â€‹Hz) performed for functional mapping of the cingulate cortex. We used two methods to characterize brain connectivity. Effective connectivity was inferred based on the analysis of cortico-cortical potentials (CCEPs) evoked by single pulse electrical stimulation (SPES) (15 â€‹s inter-pulse interval). Functional connectivity was estimated using the non-linear regression method applied to 60 â€‹s spontaneous electrical brain signal intervals. The effective (stimulation-evoked) and functional (non-evoked) connectivity analyses highlight brain networks in a different way. While non-evoked connectivity evidences areas having related activity, often in close proximity to each other, evoked connectivity highlights spatially extended networks. To highlight in a comprehensive way the cingulate cortex's network, we have performed a bi-modal connectivity analysis that combines the resting-state broadband h2 non-linear correlation with cortico-cortical evoked potentials. We co-registered the patient's anatomy with the fsaverage FreeSurfer template to perform the automatic labeling based on HCP-MMP parcellation. At a group level, connectivity was estimated by averaging responses over stimulated/recorded or recorded sites in each pair of parcels. Finally, for multiple regions that evoked a clinical response during high frequency stimulation, we combined the connectivity of individual pairs using maximum intensity projection. Connectivity was assessed by applying SPES on 2094 contact pairs and recording CCEPs on 3580 contacts out of 8582 contacts of 660 electrodes implanted in 47 patients. Clinical responses elicited by high frequency stimulations in 107 sites (pairs of contacts) located in the cingulate cortex were divided in 10 groups: affective, motor behavior, motor elementary, versive, speech, vestibular, autonomic, somatosensory, visual and changes in body perception. Anterior cingulate cortex was shown to be connected to the mesial temporal, orbitofrontal and prefrontal cortex. In the middle cingulate cortex, we located affective, motor behavior in the anterior region, and elementary motor and somatosensory in the posterior part. This region is connected to the prefrontal, premotor and primary motor network. Finally, the posterior cingulate was shown to be connected with the visual areas, mesial and lateral parietal and temporal cortex.

Illusory own body perceptions mapped in the cingulate cortex-An intracranial stimulation study.

Body awareness is the result of sensory integration in the posterior parietal cortex; however, other brain structures are part of this process. Our goal is to determine how the cingulate cortex is involved in the representation of our body. We retrospectively selected patients with drug-resistant epilepsy, explored by stereo-electroencephalography, that had the cingulate cortex sampled outside the epileptogenic zone. The clinical effects of high-frequency electrical stimulation were reviewed and only those sites that elicited changes related to body perception were included. Connectivity of the cingulate cortex and other cortical structures was assessed using the h2 coefficient, following a nonlinear regression analysis of the broadband EEG signal. Poststimulation changes in connectivity were compared between two sets of stimulations eliciting or not eliciting symptoms related to body awareness (interest and control groups). We included 17 stimulations from 12 patients that reported different types of body perception changes such as sensation of being pushed toward right/left/up, one limb becoming heavier/lighter, illusory sensation of movement, sensation of pressure, sensation of floating or detachment of one hemi-body. High-frequency stimulation in the cingulate cortex (1 anterior, 15 middle, 1 posterior part) elicits body perception changes, associated with a decreased connectivity of the dominant posterior insula and increased coupling between other structures, located particularly in the nondominant hemisphere.

Seizure onset predicts its type.

OBJECTIVE: Epilepsy is characterized by transient alterations in brain synchronization resulting in seizures with a wide spectrum of manifestations. Seizure severity and risks for patients depend on the evolution and spread of the hypersynchronous discharges. With standard visual inspection and pattern classification, this evolution could not be predicted early on. It is still unclear to what degree the seizure onset zone determines seizure severity. Such information would improve our understanding of ictal epileptic activity and the existing electroencephalogram (EEG)-based warning and intervention systems, providing specific reactions to upcoming seizure types. We investigate the possibility of predicting the future development of an epileptic seizure during the first seconds of recordings after their electrographic onset. METHODS: Based on intracranial EEG recordings of 493 ictal events from 26 patients with focal epilepsy, a set of 25 time and frequency domain features was computed using nonoverlapping 1-second time windows, from the first 3, 5, and 10 seconds of ictal EEG. Three random forest classifiers were trained to predict the future evolution of the seizure, distinguishing between subclinical events, focal onset aware and impaired awareness, and focal to bilateral tonic-clonic seizures. RESULTS: Results show that early seizure type prediction is possible based on a single EEG channel located in the seizure onset zone with correct prediction rates of 76.2 ± 14.5% for distinguishing subclinical electrographic events from clinically manifest seizures, 75 ± 16.8% for distinguishing focal onset seizures that are or are not bilateral tonic-clonic, and 71.4 ± 17.2% for distinguishing between focal onset seizures with or without impaired awareness. All predictions are above the chance level (P < .01). SIGNIFICANCE: These findings provide the basis for developing systems for specific early warning of patients and health care providers, and for targeting EEG-based closed-loop intervention approaches to electrographic patterns with a high inherent risk to become clinically manifest.

Laser ablation for mesial temporal lobe epilepsy: Surgical and cognitive outcomes with and without mesial temporal sclerosis.

OBJECTIVES: Laser interstitial thermal therapy (LITT) is a minimally invasive surgical technique for focal epilepsy. A major appeal of LITT is that it may result in fewer cognitive deficits, especially when targeting dominant hemisphere mesial temporal lobe (MTL) epilepsy. To evaluate this, as well as to determine seizure outcomes following LITT, we evaluated the relationships between ablation volumes and surgical or cognitive outcomes in 43 consecutive patients undergoing LITT for MTL epilepsy. METHODS: All patients underwent unilateral LITT targeting mesial temporal structures. FreeSurfer software was used to derive cortical and subcortical segmentation of the brain (especially subregions of the MTL) using preoperative magnetic resonance imaging (MRI). Ablation volumes were outlined using a postablation T1-contrasted MRI. The percentages of the amygdala, hippocampus, and entorhinal cortex ablated were quantified objectively. The volumetric measures were regressed against changes in neuropsychological performance before and after surgery, RESULTS: A median of 73.7% of amygdala, 70.9% of hippocampus, and 28.3% of entorhinal cortex was ablated. Engel class I surgical outcome was obtained in 79.5% and 67.4% of the 43 patients at 6 and 20.3 months of follow-up, respectively. No significant differences in surgical outcomes were found across patient subgroups (hemispheric dominance, hippocampal sclerosis, or need for intracranial evaluation). Furthermore, no significant differences in volumes ablated were found between patients with Engel class IA vs Engel class II-IV outcomes. In patients undergoing LITT in the dominant hemisphere, a decline in verbal and narrative memory, but not in naming function was noted. SIGNIFICANCE: Seizure-free outcomes following LITT may be comparable in carefully selected patients with and without MTS, and these outcomes are comparable with outcomes following microsurgical resection. Failures may result from non-mesial components of the epileptogenic network that are not affected by LITT. Cognitive declines following MTL-LITT are modest, and principally affect memory processes.

A connectomics approach combining structural and effective connectivity assessed by intracranial electrical stimulation.

In the context of the human brain, the term "connectivity" can refer to structural, functional or effective connectivity. Intracranial electrical stimulation is perhaps the most direct way of investigating the effective connectivity. We propose a method of mapping the effective connectivity, revealed by the electrical stimulation of brain structures, over the structural connectome (SC), obtained through diffusion spectrum imaging (DSI), to form a structural-effective connectome (SEC). A number of 24 patients with refractory epilepsy were implanted with depth electrodes for pre-surgical evaluation. Effective connectivity was assessed by analyzing the responses to single pulse electrical stimulation (SPES). Stimulation pulses having variable amplitude were applied to each pair of adjacent contacts and responses evoked by stimulation were recorded from other contacts located in other brain areas. Early responses (10-110 ms) on the stimulation-activated contacts located outside the epileptogenic zone were averaged for each patient, resulting in a patient-level physiological effective connectome (EC). The population level EC is computed by averaging the connections of the individual ECs, on a structure by structure basis. A fiber activation factor is used to weight the number of fibers connecting a pair of structures in the SC by its corresponding normalized EC value. The resulting number of effectively activated fibers describes the directional connection strength between two structures in the SEC. A physiological SEC comprising directional connections between 70 segmented brain areas in both hemispheres, was obtained by inclusion of structures outside the epileptogenic zone only. Over the entire structure set, the Spearman's correlation coefficient ρ between the number of fibers extracted from the DSI Atlas and the normalized RMS responses to SPES was ρ=0.21 (p<0.001), while Kendall's tau coefficients ranged -0.52-0.44 (p<0.05). The physiological structural-effective connectomics approach we have introduced can be applied for the creation of a whole-brain connectivity atlas that can be used as a reference tool for differential analysis of altered versus normal brain connectivity in epileptic patients.

A personalized stereotactic fixture for implantation of depth electrodes in stereoelectroencephalography.

BACKGROUND: The stereoelectroencephalographic (SEEG) implantation procedures still represent a challenge due to the intrinsic complexity of the method and the number of depth electrodes required. OBJECTIVES: We aim at designing and evaluating the accuracy of a custom stereotactic fixture based on the StarFix™ technology (FHC Inc., Bowdoin, ME) that significantly simplifies and optimizes the implantation of depth electrodes used in presurgical evaluation of patients with drug-resistant epilepsy. METHODS: Fiducial markers that also serve as anchors for the fixture are implanted into the patient's skull prior to surgery. A 3D fixture model is designed within the surgical planning software, with the planned trajectories incorporated in its design, aligned with the patient's anatomy. The stereotactic fixture is built using 3D laser sintering technology based on the computer-generated model. Bilateral rectangular grids of guide holes orthogonal to the midsagittal plane and centered on the midcommissural point are incorporated in the fixture design, allowing a wide selection of orthogonal trajectories. Up to two additional grids can be accommodated for targeting structures where oblique trajectories are required. The frame has no adjustable parts, this feature reducing the risk of inaccurate coordinate settings while simultaneously reducing procedure time significantly. RESULTS: We have used the fixture for the implantation of depth electrodes for presurgical evaluation of 4 patients with drug-resistant focal epilepsy, with nearly 2-fold reduction in the duration of the implantation procedure. We have obtained a high accuracy with a submillimetric mean positioning error of 0.68 mm for the anchor bolts placed at the trajectory entry point and 1.64 mm at target. CONCLUSIONS: The custom stereotactic fixture design greatly simplifies the planning procedure and significantly reduces the time in the operating room, while maintaining a high accuracy.

The spatiotemporal dynamics of semantic integration in the human brain.

Language depends critically on the integration of lexical information across multiple words to derive semantic concepts. Limitations of spatiotemporal resolution have previously rendered it difficult to isolate processes involved in semantic integration. We utilized intracranial recordings in epilepsy patients (n = 58) who read written word definitions. Descriptions were either referential or non-referential to a common object. Semantically referential sentences enabled high frequency broadband gamma activation (70-150 Hz) of the inferior frontal sulcus (IFS), medial parietal cortex, orbitofrontal cortex (OFC) and medial temporal lobe in the left, language-dominant hemisphere. IFS, OFC and posterior middle temporal gyrus activity was modulated by the semantic coherence of non-referential sentences, exposing semantic effects that were independent of task-based referential status. Components of this network, alongside posterior superior temporal sulcus, were engaged for referential sentences that did not clearly reduce the lexical search space by the final word. These results indicate the existence of complementary cortical mosaics for semantic integration in posterior temporal and inferior frontal cortex.

Studying memory processes at different levels with simultaneous depth and surface EEG recordings.

Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.

A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG.

Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods' results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure's involvement in the facial emotion recognition task.

Motor and emotional facial features in parietal lobe seizures: an ictal functional connectivity study using SEEG

Objectives: Parietal lobe epilepsy is one of the rarest types and patients with this form of epilepsy report multiple subjective symptoms during ictal manifestation. Specific facial coupling of emotion and motor symptoms may take various forms, such as pouting and disgust or smiling. We aimed to highlight brain structures and the network involved during ictal grimacing in parietal lobe seizures. Methods: In this study, we report two patients with drug-resistant epilepsy, with seizure onset located in the inferior parietal lobule and a semiology characterized by ictal grimacing. Patients were explored with intracranial electrodes using the stereo-electroencephalographic method. Time-frequency and functional connectivity (a non-linear regression method based on the h² correlation coefficient) signal analyses were performed time-locked to ictal grimace. For both patients, using spectral analysis, we were able to confirm that the bipolar channels, localized at the level of the inferior parietal lobule, were involved in the seizure onset zone, exhibiting a high frequency discharge. Results: The first patient presented with ictal pouting and disgust and the second with smiling/laughter. Connectivity analysis highlighted two different networks responsible for seizure semiology, consisting of grimacing with different emotional expression. The inferior parietal lobule, connected mainly to the anterior insula, dorsal-lateral prefrontal cortex and frontal operculum were responsible for the typical grimace associated with disgust. Furthermore, the inferior parietal lobule, basal temporal structures, superior temporal gyrus, orbitofrontal cortex and temporal pole were involved in smiling and laughter. Significance: It is of great significance for epileptologists to know that the same seizure onset zone in the inferior parietal lobule can generate contrasting facial expressions, smiling/laughter and pouting/disgust, by engaging different epileptogenic networks; the temporo-basal-orbitofrontal and insulo-opercular networks, respectively.

Network of autoscopic hallucinations elicited by intracerebral stimulations of periventricular nodular heterotopia: An SEEG study.

Periventricular nodular heterotopias (PVNH) are areas of neurons abnormally located in the white matter that might be involved in physiological cortical functions. Autoscopic hallucinations are changes in self-consciousness determined by a mismatch in integration of multiple sensory inputs. Our goal is to highlight the brain network involved in generation of autoscopic hallucination elicited by electrical stimulation of a PVNH in a drug resistant epilepsy patient. Our patient was explored using stereo-electroencephalography with electrodes covering the right posterior temporal PVNH and the adjacent cortex. Direct electrical high frequency stimulation of the PVNH elicited autoscopic hallucinations mainly involving the face and upper trunk. We then used multiple modalities to determine brain connectivity: single pulse electrical stimulation of the PVNH and stimulation-evoked potentials were used to highlight resting state effective connectivity. High-frequency stimulation using alternating polarity pulses enabled us to identify the network involved, time-locked to the clinical effect and to map symptom-related effective connectivity. Functional connectivity using a non-linear regression method was used to determine dependencies between different cortical regions following the stimulation. Finally, structural connectivity was highlighted using deterministic fiber tracking. Multi-modal connectivity analysis identified a network involving the PVNH, occipital and temporal neocortex, fusiform gyrus and parietal cortex.

Spatiotemporal dynamics of orthographic and lexical processing in the ventral visual pathway.

Reading is a rapid, distributed process that engages multiple components of the ventral visual stream. To understand the neural constituents and their interactions that allow us to identify written words, we performed direct intra-cranial recordings in a large cohort of humans. This allowed us to isolate the spatiotemporal dynamics of visual word recognition across the entire left ventral occipitotemporal cortex. We found that mid-fusiform cortex is the first brain region sensitive to lexicality, preceding the traditional visual word form area. The magnitude and duration of its activation are driven by the statistics of natural language. Information regarding lexicality and word frequency propagates posteriorly from this region to visual word form regions and to earlier visual cortex, which, while active earlier, show sensitivity to words later. Further, direct electrical stimulation of this region results in reading arrest, further illustrating its crucial role in reading. This unique sensitivity of mid-fusiform cortex to sub-lexical and lexical characteristics points to its central role as the orthographic lexicon-the long-term memory representations of visual word forms.

Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG.

The success of stereoelectroencephalographic (SEEG) investigations depends crucially on the hypotheses on the putative location of the seizure onset zone. This information is derived from non-invasive data either based on visual analysis or advanced source localization algorithms. While source localization applied to interictal spikes recorded on scalp is the classical method, it does not provide unequivocal information regarding the seizure onset zone. Raw ictal activity contains a mixture of signals originating from several regions of the brain as well as EMG artifacts, hampering direct input to the source localization algorithms. We therefore introduce a methodology that disentangles the various sources contributing to the scalp ictal activity using independent component analysis and uses equivalent current dipole localization as putative locus of ictal sources. We validated the results of our analysis pipeline by performing long-term simultaneous scalp - intracerebral (SEEG) recordings in 14 patients and analyzing the wavelet coherence between the independent component encoding the ictal discharge and the SEEG signals in 8 patients passing the inclusion criteria. Our results show that invasively recorded ictal onset patterns, including low-voltage fast activity, can be captured by the independent component analysis of scalp EEG. The visibility of the ictal activity strongly depends on the depth of the sources. The equivalent current dipole localization can point to the seizure onset zone (SOZ) with an accuracy that can be as high as 10 mm for superficially located sources, that gradually decreases for deeper seizure generators, averaging at 47 mm in the 8 analyzed patients. Independent component analysis is therefore shown to have a promising SOZ localizing value, indicating whether the seizure onset zone is neocortical, and its approximate location, or located in mesial structures. That may contribute to a better crafting of the hypotheses used as basis of the stereo-EEG implantations.

Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision.

High-frequency oscillations >80 Hz (HFOs) have unique features distinguishing them from spikes and artifactual components that can be well-evidenced in the time-frequency representations. We introduce an unsupervised HFO detector that uses computer-vision algorithms to detect HFO landmarks on two-dimensional (2D) time-frequency maps. To validate the detector, we introduce an analytical model of the HFO based on a sinewave having a Gaussian envelope, for which analytical equations in time-frequency space can be derived, allowing us to establish a direct correspondence between common HFO detection criteria in the time domain with the ones in the frequency domain, used by the computer-vision detection algorithm. The detector identifies potential HFO events on the time-frequency representation, which are classified as true HFOs if criteria regarding the HFO's frequency, amplitude, and duration are met. The detector is validated on simulated HFOs according to the analytical model, in the presence of noise, with different signal-to-noise ratios (SNRs) ranging from -9 to 0 dB. The detector's sensitivity was 0.64 at an SNR of -9 dB, 0.98 at -6 dB, and >0.99 at -3 dB and 0 dB, while its positive prediction value was >0.95, regardless of the SNR. Using the same simulation dataset, our detector is benchmarked against four previously published HFO detectors. The F-measure, a combined metric that takes into account both sensitivity and positive prediction value, was used to compare detection algorithms. Our detector surpassed the other detectors at -6, -3, and 0 dB and had the second best F-score at -9 dB SNR after the MNI detector (0.77 vs. 0.83). The ability to detect HFOs in clinical recordings has been tested on a set of 36 intracranial electroencephalogram (EEG) channels in six patients, with 89% of the detections being validated by two independent reviewers. The results demonstrate that the unsupervised detection of HFOs based on their 2D features in time-frequency maps is feasible and has a performance comparable or better than the most used HFO detectors.

Visual field deficits following laser ablation of the hippocampus.

OBJECTIVE: To qualify the incidence of and risk factors for visual field deficits (VFD) following laser interstitial thermal ablation (LITT) for mesial temporal lobe epilepsy (MTLE) and to relate this to anterior temporal lobectomy (ATL). METHODS: Fifty-seven patients underwent LITT of the amygdalo-hippocampal complex (AH) for MTLE. Masks of ablation volumes, laser probe trajectories, and visual radiations (VRs) from individual subject space were transformed into standardized space using nonlinear registration. Voxel-wise statistics were performed to model relationships between VFDs vs ablation volumes, laser trajectories, VRs, and AH asymmetry. A review of VFDs following ATLs was performed. RESULTS: The incidence of VFD after LITT is much lower than after ATLs. A total of 37.5% of patients developed a VFD, with the probability of this being much higher after left (50%) vs right hemisphere LITT (10%) (Fisher test, p = 0.05). This laterality effect on VFDs is mirrored but underappreciated in ATL series. The most consistent LITT-VFD occurred in the superior vertical octant. Ablation of Meyer loop as well as the summed probability of VRs within laser trajectories correlated with VFDs (p < 0.05). Left and right hippocampi have significantly distinct orientations in axial and coronal planes, which may be one reason for the variation in VFD probability. CONCLUSIONS: LITT results in lower rates of and smaller VFDs-typically an octantanopsia. VRs are at greater risk during surgery for left than right MTLE. Anatomical asymmetries in hippocampal anatomy may explain the hemispheric differences in deficits, and should factor into trajectory planning and also into preoperative patient counseling. Overall the incidence and extent of visual deficits following LITT for MTLE is lower than the reported data following anterior temporal lobectomy. VF tractography incorporated into LITT planning may reduce the occurrence of VFDs.

Sleep modulates effective connectivity: A study using intracranial stimulation and recording.

OBJECTIVE: Sleep is an active process with an important role in memory. Epilepsy patients often display a disturbed sleep architecture, with consequences on cognition. We aimed to investigate the effect of sleep on cortical networks' organization. METHODS: We analyzed cortico-cortical evoked responses elicited by single pulse electrical stimulation (SPES) using intracranial depth electrodes in 25 patients with drug-resistant focal epilepsy explored using stereo-EEG. We applied the SPES protocol during wakefulness and NREM - N2 sleep. We analyzed 31,710 significant responses elicited by 799 stimulations covering most brain structures, epileptogenic or non-epileptogenic. We analyzed effective connectivity between structures using a graph-theory approach. RESULTS: Sleep increases excitability in the brain, regardless of epileptogenicity. Local and distant connections are differently modulated by sleep, depending on the tissue epileptogenicity. In non-epileptogenic areas, frontal lobe connectivity is enhanced during sleep. There is increased connectivity between the hippocampus and temporal neocortex, while perisylvian structures are disconnected from the temporal lobe. In epileptogenic areas, we found a clear interhemispheric difference, with decreased connectivity in the right hemisphere during sleep. CONCLUSIONS: Sleep modulates brain excitability and reconfigures functional brain networks, depending on tissue epileptogenicity. SIGNIFICANCE: We found specific patterns of information flow during sleep in physiologic and pathologic structures, with possible implications for cognition.