Critical Language Areas Show Increased Functional Connectivity in Human Cortex

Intracerebral electrical stimulation of the face-selective right lateral fusiform gyrus transiently impairs face identity recognition

Neuroimaging and intracranial electrophysiological studies have consistently shown the largest and most consistent face-selective neural activity in the middle portion of the human right lateral fusiform gyrus ('fusiform face area(s)', FFA). Yet, direct evidence for the critical role of this region in face identity recognition (FIR) is still lacking. Here we report the first evidence of transient behavioral impairment of FIR during focal electrical stimulation of the right FFA. Upon stimulation of an electrode contact within this region, subject CJ, who shows typical FIR ability outside of stimulation, was transiently unable to point to pictures of famous faces among strangers and to match pictures of famous or unfamiliar faces presented simultaneously for their identity. Her performance at comparable tasks with other visual materials (written names, pictures of buildings) remained unaffected by stimulation at the same location. During right FFA stimulation, CJ consistently reported that simultaneously presented faces appeared as being the same identity, with little or no distortion of the spatial face configuration. Independent electrophysiological recordings showed the largest neural face-selective and face identity activity at the critical electrode contacts. Altogether, this extensive multimodal case report supports the causal role of the right FFA in FIR.

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

Reorganization and Plasticity of the Language Network in Patients with Cerebral Gliomas

Language is organized in large-scale networks in the human brain that show a strong potential for flexible interactions and adaptation. Neuroplasticity is the central mechanism that allows such dynamic modulation to changing conditions across the life span and is particularly important for network reorganization after brain lesions. Most studies on language reorganization focused on language recovery after stroke. Yet, a strong degree of adaptive neuroplasticity can also be observed in patients with brain tumors in language-eloquent brain areas. This review discusses key mechanisms for neural reorganization in patients with brain tumors. Our main aim is to elucidate the underlying mechanisms for intra- and interhemispheric plasticity in the language network in these patients. The following reorganization patterns are discussed: 1) Persisting function within the tumor; 2) Reorganization in perilesional regions; 3) Reorganization in a distributed network of the affected hemisphere; 4) Reorganization to the contralesional hemisphere. In this context, we shed light on language-related reorganization patterns in frontal and temporo-parietal areas and discuss their functional relevance. We also address tumor-related changes in structural and functional connectivity between eloquent brain regions. Thereby, we aim to expand the general understanding of the plastic potential of the neural language network and facilitate clinical decision-making processes for effective, function-preserving tumor treatment.

Naming impairments evoked by focal cortical electrical stimulation in the ventral temporal cortex correlate with increased functional connectivity

BACKGROUND

High-frequency cortical electrical stimulations (HF-CES) are the gold standard for presurgical functional mapping. In the dominant ventral temporal cortex (VTC) HF-CES can elicit transient naming impairment (eloquent sites), defining a basal temporal language area (BTLA).

OBJECTIVE

Whether naming impairments induced by HF-CES within the VTC are related to a specific pattern of connectivity of the BTLA within the temporal lobe remains unknown. We addressed this issue by comparing the connectivity of eloquent and non-eloquent sites from the VTC using cortico-cortical evoked potentials (CCEP).

METHODS

Low frequency cortical electrical stimulations (LF-CES) were used to evoke CCEP in nine individual brains explored with Stereo-Electroencephalography. We compared the connectivity of eloquent versus non eloquent sites within the VTC using Pearson's correlation matrix.

RESULTS

Overall, within the VTC, eloquent sites were associated with increased functional connectivity compared to non-eloquent sites. Among the VTC structures, this pattern holds true for the inferior temporal gyrus and the parahippocampal gyrus while the fusiform gyrus specifically showed a high connectivity in both non eloquent and eloquent sites.

CONCLUSIONS

Our findings suggest that the cognitive effects of focal HF-CES are related to the functional connectivity properties of the stimulated sites, and therefore to the disturbance of a wide cortical network. They further suggest that functional specialization of a cortical region emerges from its specific pattern of functional connectivity. Cortical electrical stimulation functional mapping protocols including LF coupled to HF-CES could provide valuable data characterizing both local and distant functional architecture.

Intracerebral electrical stimulation to understand the neural basis of human face identity recognition

Recognizing people's identity by their faces is a key function in the human species, supported by regions of the ventral occipito-temporal cortex (VOTC). In the last decade, there have been several reports of perceptual face distortion during direct electrical stimulation (DES) with subdural electrodes positioned over a well-known face-selective VOTC region of the right lateral middle fusiform gyrus (LatMidFG; i.e., the "Fusiform Face Area", FFA). However, transient impairments of face identity recognition (FIR) have been extremely rare and only behaviorally quantified during DES with intracerebral (i.e., depth) electrodes in stereo-electroencephalography (SEEG). The three detailed cases reported so far, summarized here, were specifically impaired at FIR during DES inside different anatomical VOTC regions of the right hemisphere: the inferior occipital gyrus (IOG) and the LatMidFG, as well as a region that lies at the heart of a large magnetic susceptibility artifact in functional magnetic resonance imaging (fMRI): the anterior fusiform gyrus (AntFG). In the first two regions, the eloquent electrode contacts were systematically associated with the highest face-selective and (unfamiliar) face individuation responses as measured with intracerebral electrophysiology. Stimulation in the right AntFG did not lead to perceptual changes but also caused an inability to remember having been presented face pictures, as if the episode was never recorded in memory. These observations support the view of an extensive network of face-selective VOTC regions subtending human FIR, with at least three critical nodes in the right hemisphere associated with differential intrinsic and extrinsic patterns of reentrant connectivity.

Resting-State SEEG May Help Localize Epileptogenic Brain Regions

BACKGROUND

Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures.

OBJECTIVE

To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings.

METHODS

In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions.

RESULTS

Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%.

CONCLUSION

Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy

OBJECTIVE

In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity.

METHODS

In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity.

RESULTS

Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes.

SIGNIFICANCE

Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.

The impact of high functional connectivity network hub resection on language task performance in adult low- and high-grade glioma

OBJECTIVE

Gliomas are intrinsic brain tumors with the hallmark of diffuse white matter infiltration, resulting in short- and long-range network dysfunction. Preoperative magnetoencephalography (MEG) can assist in maximizing the extent of resection while minimizing morbidity. While MEG has been validated in motor mapping, its role in speech mapping remains less well studied. The authors assessed how the resection of intraoperative electrical stimulation (IES)-negative, high functional connectivity (HFC) network sites, as identified by MEG, impacts language performance.

METHODS

Resting-state, whole-brain MEG recordings were obtained from 26 patients who underwent perioperative language evaluation and glioma resection that was guided by awake language and IES mapping. The functional connectivity of an individual voxel was determined by the imaginary coherence between the index voxel and the rest of the brain, referenced to its contralesional pair. The percentage of resected HFC voxels was correlated with postoperative language outcomes in tasks of increasing complexity: text reading, 4-syllable repetition, picture naming, syntax (SYN), and auditory stimulus naming (AN).

RESULTS

Overall, 70% of patients (14/20) in whom any HFC tissue was resected developed an early postoperative language deficit (mean 2.3 days, range 1-8 days), compared to 33% of patients (2/6) in whom no HFC tissue was resected (p = 0.16). When bifurcated by the amount of HFC tissue that was resected, 100% of patients (3/3) with an HFC resection > 25% displayed deficits in AN, compared to 30% of patients (6/20) with an HFC resection < 25% (p = 0.04). Furthermore, there was a linear correlation between the severity of AN and SYN decline with percentage of HFC sites resected (p = 0.02 and p = 0.04, respectively). By 2.2 months postoperatively (range 1-6 months), the correlation between HFC resection and both AN and SYN decline had resolved (p = 0.94 and p = 1.00, respectively) in all patients (9/9) except two who experienced early postoperative tumor progression or stroke involving inferior frontooccipital fasciculus.

CONCLUSIONS

Imaginary coherence measures of functional connectivity using MEG are able to identify HFC network sites within and around low- and high-grade gliomas. Removal of IES-negative HFC sites results in early transient postoperative decline in AN and SYN, which resolved by 3 months in all patients without stroke or early tumor progression. Measures of functional connectivity may therefore be a useful means of counseling patients about postoperative risk and assist with preoperative surgical planning.

Cortical stimulation in aphasia following ischemic stroke: toward model-guided electrical neuromodulation

The aim of this paper is to integrate different bodies of research including brain traveling waves, brain neuromodulation, neural field modeling and post-stroke language disorders in order to explore the opportunity of implementing model-guided, cortical neuromodulation for the treatment of post-stroke aphasia. Worldwide according to WHO, strokes are the second leading cause of death and the third leading cause of disability. In ischemic stroke, there is not enough blood supply to provide enough oxygen and nutrients to parts of the brain, while in hemorrhagic stroke, there is bleeding within the enclosed cranial cavity. The present paper focuses on ischemic stroke. We first review accumulating observations of traveling waves occurring spontaneously or triggered by external stimuli in healthy subjects as well as in patients with brain disorders. We examine the putative functions of these waves and focus on post-stroke aphasia observed when brain language networks become fragmented and/or partly silent, thus perturbing the progression of traveling waves across perilesional areas. Secondly, we focus on a simplified model based on the current literature in the field and describe cortical traveling wave dynamics and their modulation. This model uses a biophysically realistic integro-differential equation describing spatially distributed and synaptically coupled neural networks producing traveling wave solutions. The model is used to calculate wave parameters (speed, amplitude and/or frequency) and to guide the reconstruction of the perturbed wave. A stimulation term is included in the model to restore wave propagation to a reasonably good level. Thirdly, we examine various issues related to the implementation model-guided neuromodulation in the treatment of post-stroke aphasia given that closed-loop invasive brain stimulation studies have recently produced encouraging results. Finally, we suggest that modulating traveling waves by acting selectively and dynamically across space and time to facilitate wave propagation is a promising therapeutic strategy especially at a time when a new generation of closed-loop cortical stimulation systems is about to arrive on the market.

Connectivity strength, time lag structure and the epilepsy network in resting-state fMRI

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Stereo-encephalography informed high-resolution functional connectome analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls.

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Specific patterns of altered connectivity include:.○

broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain.

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decreased time lag within the seizure onset node.

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globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation.

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Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.

The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.

A SENtence Supramodal Areas AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic connectivity in 144 healthy right-handers

We herein propose an atlas of 32 sentence-related areas based on a 3-step method combining the analysis of activation and asymmetry during multiple language tasks with hierarchical clustering of resting-state connectivity and graph analyses. 144 healthy right-handers performed fMRI runs based on language production, reading and listening, both with sentences and lists of over-learned words. Sentence minus word-list BOLD contrast and left-minus-right BOLD asymmetry for each task were computed in pairs of homotopic regions of interest (hROIs) from the AICHA atlas. Thirty-two hROIs were identified that were conjointly activated and leftward asymmetrical in each of the three language contrasts. Analysis of resting-state temporal correlations of BOLD variations between these 32 hROIs allowed the segregation of a core network, SENT_CORE including 18 hROIs. Resting-state graph analysis applied to SENT_CORE hROIs revealed that the pars triangularis of the inferior frontal gyrus and the superior temporal sulcus were hubs based on their degree centrality (DC), betweenness, and participation values corresponding to epicentres of sentence processing. Positive correlations between DC and BOLD activation values for SENT_CORE hROIs were observed across individuals and across regions regardless of the task: the more a SENT_CORE area is connected at rest the stronger it is activated during sentence processing. DC measurements in SENT_CORE may thus be a valuable index for the evaluation of inter-individual variations in language areas functional activity in relation to anatomical or clinical patterns in large populations. SENSAAS (SENtence Supramodal Areas AtlaS), comprising the 32 supramodal sentence areas, including SENT_CORE network, can be downloaded at http://www.gin.cnrs.fr/en/tools/ .