• Neuroinformatics
• 2019-2023close
• Open Access close

AbstractBOLD fMRI is widely applied in human neuroscience but is limited in its spatial specificity due to a cortical‐depth‐dependent venous bias. This reduces its localization specificity with respect to neuronal responses, a disadvantage for neuroscientific research. Here, we modified a submillimeter BOLD protocol to selectively reduce venous and tissue signal and increase cerebral blood volume weighting through a pulsed saturation scheme (dubbed Arterial Blood Contrast) at 7 T. Adding Arterial Blood Contrast on top of the existing BOLD contrast modulated the intracortical contrast. Isolating the Arterial Blood Contrast showed a response free of pial‐surface bias. The results suggest that Arterial Blood Contrast can modulate the typical fMRI spatial specificity, with important applications in in‐vivo neuroscience.

Abstract Background Fine motor and coordination problems are frequently reported among adolescents born preterm. We aimed to assess performance in hand coordination tasks and to compare concurrent brain activation between adolescents born very preterm and at term at 13 years. Methods A total of 34 right-handed adolescents born very preterm (gestational age less than 32 weeks/birth weight ≤1500 grams) and 37 controls born at term during 2003 to 2006 in Turku University Hospital, Finland, were recruited. At 13 years, finger opposition and diadochokinesis were assessed, and brain functional magnetic resonance imaging data were acquired while the adolescents performed unimanual hand coordination tasks in response to visual cue. Results Adolescents born very preterm performed similar to controls in hand coordination tasks. The very preterm group evoked greater brain activation than the controls in the right precentral gyrus and in the right postcentral gyrus during left-hand finger opposition and in the right postcentral gyrus during left-hand diadochokinesis. Within the very preterm group, lower gestational age was associated with reduced activation in the left superior parietal lobule during right-hand diadochokinesis. Regarding left-hand tasks, lower gestational age was associated with stronger activation in the right cerebellar lobule V and left cerebellar lobule VI during finger opposition and stronger activation in the right superior parietal lobule during diadochokinesis. Conclusions Very preterm birth affected hand coordination-related brain activation. Most of the effects were found for nondominant hand. Clinical performance during the hand coordination tasks was similar in adolescents born very preterm and controls.

Pain is an important non-motor symptom of Parkinson’s disease (PD). It negatively impacts the quality of life. However, the pathophysiological mechanisms underlying pain in PD remain to be elucidated. This study sought to use electroencephalographic (EEG) coherence analysis to compare neuronal synchronization in neuronal networks between patients with PD, with and without pain. Twenty-four patients with sporadic PD were evaluated for the presence of pain. Time-frequency and coherence analyses were performed on their EEG data. Whole-brain and regional coherence were calculated and compared between pain-positive and pain-negative patients. There was no significant difference in the whole-brain coherence between the pain-positive and pain-negative groups. However, temporal–temporal coherence differed significantly between the two groups (p = 0.031). Our findings indicate that aberrant synchronization of inter-temporal regions is involved in PD-related pain. This will further our understanding of the mechanisms underlying pain in PD.

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Childhood primary angiitis of the central nervous system (cPACNS) is a rare inflammatory disease of brain vessels. The small vessel subtype is diagnosed on brain biopsy and often presents with cognitive and behavioral changes, headaches, and seizures. However, there are few reported cases of super-refractory status epilepticus in children. We present a case of small vessel cPACNS complicated by super-refractory status epilepticus requiring burst suppression for 4 weeks in addition to multiple antiseizure medications and the ketogenic diet. Our patient was also treated with intravenous and oral steroids, intravenous immunoglobulin, and cyclophosphamide before starting maintenance therapy with mycophenolate mofetil. After prolonged rehabilitation, he recovered almost completely and has a normal neurologic examination with rare epileptiform activity on electroencephalogram (EEG). This is one of the longest cases of status epilepticus in small vessel cPACNS in the literature. We illustrate that super-refractory status epilepticus can be the first manifestation of small vessel cPACNS in previously healthy children and that symptomatic management of seizures with concurrent immunosuppression to treat the underlying pathology resulted in favorable neurologic outcomes.

Abstract. In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database—a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

Abstract The right inferior frontal cortex (IFC) is critical to response inhibition. The right IFC referred in the human studies of response inhibition is located in the posterior part of the inferior frontal gyrus and the surrounding regions and consists of multiple areas that implement distinct functions. Recent studies using resting-state functional connectivity have parcellated the cerebral cortex and revealed across-subject variability of parcel-based cerebrocortical networks. However, how the right IFC of individual brains is functionally organized and what functional properties the IFC parcels possess regarding response inhibition remain elusive. In the present functional magnetic resonance imaging study, precision functional mapping of individual human brains was adopted to the parcels in the right IFC to evaluate their functional properties related to response inhibition. The right IFC consisted of six modules or subsets of subregions, and the spatial organization of the modules varied considerably across subjects. Each module revealed unique characteristics of brain activity and its correlation to behavior related to response inhibition. These results provide updated functional features of the IFC and demonstrate the importance of individual-focused approaches in studying response inhibition in the right IFC.