Patient-specific Connectivity Research Articles

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

1248 Optimal Connectivity Map Predicts Seizure Freedom Rates After Laser Interstitial Thermal Therapy in Mesial Temporal Lobe Epilepsy

INTRODUCTION: Laser Interstitial Thermal Therapy (LiTT) is increasingly becoming a first-line surgical therapy for mesial temporal lobe epilepsy (mTLE). While promising, its seizure outcomes have also been highly variable, presumably due to an incomplete understanding of mechanisms behind LiTT. Epilepsy is a network disorder, and therefore it is crucial to account for both the effect on local destruction of epileptogenic foci, as well as global intervention of limbic circuitry, to elucidate the circuit-level mechanisms of LiTT and devise improved ablation strategies. In this study, we explore LiTT's network-level effect by investigating functional connectivity patterns and identifying optimal connectivity patterns predictive of seizure freedom. METHODS: 27 mTLE patients who underwent unilateral LiTT were classified as seizure-free (ILAE Class I) or seizure-present (ILAE Class II–VI) cohort based on one-year postoperative outcomes. Individual ablation volumes were segmented from intraoperative MRI sequences. Ablation volumes were seeded into a normative functional connectome (Brain Genomics Superstruct Project) to extract patient-specific connectivity profiles associated with ablation volumes. FC patterns were compared between the two cohorts to produce voxel-wise “t-maps” to identify local and global differences in connectivity strengths predictive of seizure freedom. RESULTS: Ten mTLE patients achieved complete seizure freedom. In seizure-free patients, right ablated volumes shared higher FC with regions such as calcarine, cuneus, and thalamic anterior pulvinar nucleus than patients with seizure presence. CONCLUSIONS: For the right-sided ablations, seizure freedom was associated with a distinctive whole-brain connectivity pattern beyond the local surgical target. Further investigation is required to explore if this pattern is specific between the two surgical sides in mTLE population.

Normative vs. patient-specific brain connectivity in deep brain stimulation

Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement.Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely patient-specific diffusion-MRI data, age- and disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were calculated and used to estimate clinical improvement in out of sample data.All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on this present novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made.Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of explaining slightly more variance than group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.

Connectivity Profile Predictive of Effective Deep Brain Stimulation in Obsessive-Compulsive Disorder

BackgroundDeep brain stimulation for obsessive-compulsive disorder is a rapidly developing treatment strategy for treatment-refractory patients. Both the exact target and impact on distributed brain networks remain a matter of debate. Here, we investigated which regions connected to stimulation sites contribute to clinical improvement effects and whether connectivity is able to predict outcomes. MethodsWe analyzed 22 patients (13 female) with treatment-refractory obsessive-compulsive disorder undergoing deep brain stimulation targeting the anterior limb of the internal capsule/nucleus accumbens. We calculated stimulation-dependent optimal connectivity separately for patient-specific connectivity data of 10 patients and for 12 additional patients using normative connectivity. Models of optimal connectivity were subsequently used to predict outcome in both an out-of-sample cross-validation and a leave-one-out cross-validation across the whole group. ResultsThe resulting models successfully cross-predicted clinical outcomes of the respective other sample, and a leave-one-out cross-validation across the whole group further demonstrated robustness of our findings (r = .630, p < .001). Specifically, the degree of connectivity between stimulation sites and medial and lateral prefrontal cortices significantly predicted clinical improvement. Finally, we delineated a frontothalamic pathway that is crucial to be modulated for beneficial outcome. ConclusionsSpecific connectivity profiles, encompassing frontothalamic streamlines, can predict clinical outcome of deep brain stimulation for obsessive-compulsive disorder. After further validation, our findings may be used to guide both deep brain stimulation targeting and programming and to inform noninvasive neuromodulation targets for obsessive-compulsive disorder.

Functional Connectivity in Frontoparietal Network: Indicator of Preoperative Cognitive Function and Cognitive Outcome Following Surgery in Patients with Glioma.

Patients with diffuse glioma are known to have impaired cognitive functions preoperatively. However, the mechanism of these cognitive deficits remains unclear. Resting-state functional connectivity in the frontoparietal network (FPN) is associated with cognitive performance in healthy subjects. For this reason, it was hypothesized that functional connectivity of the FPN would be related to cognitive functioning in patients with glioma. To assess this relationship, preoperative cognitive status was correlated to patient-specific connectivity within the FPN. Further, we assessed whether connectivity could predict neuropsychologic outcome following surgery. Sixteen patients with diffuse glioma underwent neuropsychologic assessment and preoperative functional magnetic resonance imaging using task (n-back) and resting-state scans. Thirteen patients had postoperative cognitive assessment. An index of patient-specific functional connectivity in the FPN was derived by averaging connectivity values between 2 prefrontal and 2 parietal cortex regions defined by activation during the n-back task. The relationship of these indices with cognitive performance was assessed. Higher average connectivity within the FPN is associated with lower composite cognitive scores. Higher connectivity of the parietal region of the tumor-affected hemisphere is associated specifically with lower fluid cognition. Lower connectivity of the parietal region of the nontumor hemisphere is associated with worse neuropsychologic outcome 1 month after surgery. Resting-state functional connectivity between key regions of the FPN is associated with cognitive performance in patients with glioma and is related to cognitive outcome following surgery.

GP.02 Resting state functional connectivity: a biomarker for pre-operative cognitive function and cognitive outcome following surgery in patients with diffuse glioma

Background: Resting state functional connectivity (RSFC) in the fronto-parietal network (FPN) has been associated with cognitive ability. For this reason, it was hypothesized that RSFC connectivity of the FPN would be related to cognition in patients with diffuse glioma. To assess this relationship, pre-operative cognitive status was correlated to patient specific connectivity within the FPN. Further, we assessed whether RSFC could predict neuropsychological outcome following surgery Methods: Sixteen patients with diffuse glioma underwent neuropsychological assessment and pre-operative task and resting state fMRI. Thirteen had post-operative cognitive assessment at one-month post-surgery. RSFC in a subject-specific FPN was correlated with pre- and post-operative cognitive scores. Results: Higher connectivity within the FPN was associated with lower composite cognitive scores, while higher connectivity of the parietal node of the tumor-affected hemisphere was associated with lower fluid cognition but not crystallized cognition. Higher connectivity values between the parietal node of the healthy hemisphere and the rest of the FPN was associated with better neuropsychological outcome one month after surgery. Conclusions: RSFC between key nodes of the FPN is associated with cognitive performance in patients with diffuse glioma and is a promising biomarker for cognitive outcome following surgery.

Individual brain structure and modelling predict seizure propagation.

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions.

Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings.

Among individuals diagnosed with schizophrenia, approximately 20%–33% are recognized as treatment-resistant schizophrenia (TRS) patients. These TRS patients suffer more severely from the disease but struggle to benefit from existing antipsychotic treatments. A few recent studies suggested that schizophrenia may be caused by impaired synaptic plasticity that manifests as functional dysconnectivity in the brain, however, few of those studies focused on the functional connectivity changes in the brains of TRS groups. In this study, we compared the whole brain connectivity variations in TRS patients, their unaffected siblings, and healthy controls. Connectivity network features between and within the 116 automated anatomical labeling (AAL) brain regions were calculated and compared using maps created with three contrasts: patient vs. control, patient vs. sibling, and sibling vs. control. To evaluate the predictive power of the selected features, we performed a multivariate classification approach. We also evaluated the influence of six important clinical measures (e.g. age, education level) on the connectivity features. This study identified abnormal significant connectivity changes of three patterns in TRS patients and their unaffected siblings: 1) 69 patient-specific connectivity (PCN); 2) 102 shared connectivity (SCN); and 3) 457 unshared connectivity (UCN). While the first two patterns were widely reported by previous non-TRS specific studies, we were among the first to report widespread significant connectivity differences between TRS patient groups and their healthy sibling groups. Observations of this study may provide new insights for the understanding of the neurophysiological mechanisms of TRS.

Patient-specific connectivity pattern of epileptic network in frontal lobe epilepsy.

There is evidence that focal epilepsy may involve the dysfunction of a brain network in addition to the focal region. To delineate the characteristics of this epileptic network, we collected EEG/fMRI data from 23 patients with frontal lobe epilepsy. For each patient, EEG/fMRI analysis was first performed to determine the BOLD response to epileptic spikes. The maximum activation cluster in the frontal lobe was then chosen as the seed to identify the epileptic network in fMRI data. Functional connectivity analysis seeded at the same region was also performed in 63 healthy control subjects. Nine features were used to evaluate the differences of epileptic network patterns in three connection levels between patients and controls. Compared with control subjects, patients showed overall more functional connections between the epileptogenic region and the rest of the brain and higher laterality. However, the significantly increased connections were located in the neighborhood of the seed, but the connections between the seed and remote regions actually decreased. Comparing fMRI runs with interictal epileptic discharges (IEDs) and without IEDs, the patient-specific connectivity pattern was not changed significantly. These findings regarding patient-specific connectivity patterns of epileptic networks in FLE reflect local high connectivity and connections with distant regions differing from those of healthy controls. Moreover, the difference between the two groups in most features was observed in the strictest of the three connection levels. The abnormally high connectivity might reflect a predominant attribute of the epileptic network, which may facilitate propagation of epileptic activity among regions in the network.

Ictal‐onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy

Fifteen percent to 25% of patients with refractory epilepsy require invasive video-electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal-onset zone. This delineation based on the recorded intracranial EEG (iEEG) signals occurs visually by the epileptologist and is therefore prone to human mistakes. The purpose of this study is to investigate whether effective connectivity analysis of intracranially recorded EEG during seizures provides an objective method to localize the ictal-onset zone. In this study data were analyzed from eight patients who underwent IVEM at Ghent University Hospital in Belgium. All patients had a focal ictal onset and were seizure-free following resective surgery. The effective connectivity pattern was calculated during the first 20 s of ictal rhythmic iEEG activity. The out-degree, which is reflective of the number of outgoing connections, was calculated for each electrode contact for every single seizure during these 20 s. The seizure specific out-degrees were summed per patient to obtain the total out-degree. The electrode contact with the highest total out-degree was considered indicative of localization of the ictal-onset zone. This result was compared to the conclusion of the visual analysis of the epileptologist and the resected brain region segmented from postoperative magnetic resonance imaging (MRI). In all eight patients the electrode contact with the highest total out-degree was among the contacts identified by the epileptologist as the ictal onset. This contact, that we named "the driver," always laid within the resected brain region. Furthermore, the patient-specific connectivity patterns were consistent over the majority of seizures. In this study we demonstrated the feasibility of correctly localizing the ictal-onset zone from iEEG recordings by using effective connectivity analysis during the first 20 s of ictal rhythmic iEEG activity.

Circular representation of human cortical networks for subject and population-level connectomic visualization

Cortical network architecture has predominantly been investigated visually using graph theory representations. In the context of human connectomics, such representations are not however always satisfactory because canonical methods for vertex-edge relationship representation do not always offer optimal insight regarding functional and structural neural connectivity. This article introduces an innovative framework for the depiction of human connectomics by employing a circular visualization method which is highly suitable to the exploration of central nervous system architecture. This type of representation, which we name a 'connectogram', has the capability of classifying neuroconnectivity relationships intuitively and elegantly. A multimodal protocol for MRI/DTI neuroimaging data acquisition is here combined with automatic image segmentation to (1) extract cortical and non-cortical anatomical structures, (2) calculate associated volumetrics and morphometrics, and (3) determine patient-specific connectivity profiles to generate subject-level and population-level connectograms. The scalability of our approach is demonstrated for a population of 50 adults. Two essential advantages of the connectogram are (1) the enormous potential for mapping and analyzing the human connectome, and (2) the unconstrained ability to expand and extend this analysis framework to the investigation of clinical populations and animal models.