Biology subjects

Paulo, D. L.

Publications and source records attributed to Paulo, D. L..

4 recordsLinked to original sources

bioRxiv2022

Corticostriatal Beta Power Changes Associated with Cognitive Function in Parkinsons Disease

Cognitive impairment (CI) is the most frequent nonmotor symptom in Parkinsons Disease (PD) and is associated with deficits in executive functions such as working memory. Previous studies have demonstrated that caudate beta power is involved in learning and working memory. Decreased dopamine in motor cortico-striato-thalamo-cortical (CSTC) circuits results in increased beta power and PD motor symptoms. Analogous changes in cognitive CSTC circuits, including the caudate and dorsolateral prefrontal cortex (DLPFC), may contribute to PD CI. The objective of our study is to evaluate whether beta power changes in caudate and DLPFC contribute to cognitive impairment in PD patients. To investigate this, we used local field potential (LFP) recordings during deep brain stimulation surgery in 15 PD patients. LFP signals from DLPFC and caudate were performed at rest and during a verbal working memory task. We examined beta power changes during the working memory task and relationship of beta power to pre-operative neuropsychological testing results. Beta power decreased in both DLPFC and caudate during encoding of correct trials, whereas beta power increased in DLPFC and caudate during feedback for correct responses. Subjects with cognitive impairment showed smaller decreases in caudate and DLPFC beta power during encoding, greater increases in beta power during feedback, and lower average resting-state beta power. Additionally, reduced caudate beta power during encoding correlated with better memory scores on pre-operative neuropsychological testing, while greater DLPFC beta power during feedback correlated with worse scores in the attention domain. Our findings suggest that similar to the relationship between beta power in motor CSTC circuits and PD motor symptoms, beta power changes in parallel cognitive CSTC circuits may be correlated with cognitive symptoms in PD patients.

neuroscience↗

bioRxiv2022

Deep learning segmentation of the nucleus basalis of Meynert on 3T MRI

The nucleus basalis of Meynert (NBM) is a key subcortical structure that is important in arousal, cognition, brain network modulation, and has been explored as a deep brain stimulation target. It has also been implicated in several disease states, including Alzheimers disease, Parkinsons disease, and temporal lobe epilepsy (TLE). Given the small size of NBM and variability between patients, NBM is difficult to study; thus, accurate, patient-specific segmentation is needed. We investigated whether a deep learning network could produce accurate, patient-specific segmentations of NBM on commonly utilized 3T MRI. It is difficult to accurately segment NBM on 3T MRI, with 7T being preferred. Paired 3T and 7T MRI datasets of 21 healthy subjects were obtained, with 6 completely withheld for testing. NBM was expertly segmented on 7T MRI, providing accurate labels for the paired 3T MRI. An external dataset of 14 patients with TLE was used to test the model on brains with neurological disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed. The model was evaluated on healthy subjects using the held-out test dataset and the external dataset of TLE patients. The model demonstrated significantly improved dice coefficient over the standard probabilistic atlas for both healthy subjects (0.68MEAN{+/-}0.08SD vs. 0.47{+/-}0.06, p=0.0089, t-test) and TLE patients (0.63{+/-}0.08 vs. 0.38{+/-}0.19, p=0.0001). Additionally, the centroid distance was significantly decreased when using the model in patients with TLE (1.22{+/-}0.33mm, 3.25{+/-}2.57mm, p=0.0110). We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the NBM.

neuroscience↗

bioRxiv2022

The Interictal Suppression Hypothesis in Focal Epilepsy: Electrographic and Structural Evaluation

Why are people with focal epilepsy not constantly seizing? Previous molecular work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is the high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure onset zones have an increased inward connectivity. Accordingly, we hypothesize that seizure onset zones are actively suppressed by the rest of the brain network during interictal states. We tested this hypothesis on 81 subjects with drug resistant focal epilepsy undergoing presurgical evaluation. We utilized intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, propagative, and non-involved regions. We then utilized diffusion imaging to acquire estimates of white matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, using our observations, we generated a resting-state classification model to assist clinicians in detecting seizure onset and propagative zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and propagative zones demonstrate markedly increased inward connectivity and decreased outward connectivity on both resting-state and neurostimulation analyses. When controlling for distance between regions, the difference between inward vs. outward connectivity remained stable up to 80 mm between brain connections. Structure-function coupling analyses revealed that seizure onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue. Using these observations, our classification models achieved a maximum held-out testing set accuracy of 92.0{+/-}2.2%. These results indicate 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 resting-sate recordings to reduce patient morbidity and augment the presurgical evaluation of drug resistant epilepsy. Furthermore, 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.

neuroscience↗

bioRxiv2022

Localizing temporal lobe seizure onset zones using deep learning on SEEG cortico-cortical evoked potentials

In drug resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization using brief interictal recordings would supplement presurgical evaluations and improve care. Thus, we sought to localize SOZs by training a multi-channel convolutional neural network on stereo-EEG (SEEG) cortico-cortical evoked potentials. We performed single pulse electrical stimulation with 10 drug resistant temporal lobe epilepsy patients implanted with SEEG. Using the 500,000 unique post-stimulation SEEG epochs, we trained a multi-channel one-dimensional convolutional neural network to determine whether an SOZ was stimulated. SOZs were classified with a mean leave-one-patient-out testing sensitivity of 78.1% and specificity of 74.6%. To achieve maximum accuracy, the model requires a 0-350 ms post stimulation time period. Post-hoc analysis revealed that the model accurately classified unilateral vs bilateral mesial temporal lobe seizure onset, and neocortical SOZs. This is the first demonstration, to our knowledge, that a deep learning framework can be used to accurately classify SOZs using cortico-cortical evoked potentials. Our findings suggest accurate classification of SOZs relies on a complex temporal evolution of evoked potentials within 350 ms of stimulation. Validation in a larger dataset could provide a practical clinical tool for the presurgical evaluation of drug resistant epilepsy.

neuroscience↗