The aim of our study was to investigate two inter-related hypotheses about the role of the subthalamic nucleus. First that the subthalamic nucleus plays a role in adjusting response thresholds and speed-accuracy trade-offs and second that it is involved in reactive and proactive inhibition and conflict resolution. These were addressed by comparing the performance of 10 patients with Parkinson’s disease treated with right subthalamotomy and 12 patients with left subthalamotomy, to 14 unoperated patients with Parkinson’s disease and 23 age-matched healthy control participants on a conditional stop signal task and applying the drift diffusion model. Unilateral subthalamotomy significantly improved Parkinson’s disease motor signs. Patients with right subthalamotomy had significantly faster Go reaction times with their contra-lesional hand than the unoperated patients and did not differ from the control participants, indicating their speed of response initiation was ‘normalized’. However, operated patients made significantly more discrimination errors than unoperated patients and controls, suggesting that subthalamotomy influenced speed-accuracy trade-offs. This was confirmed by the drift diffusion model, revealing that while the unoperated patients had significantly lower drift rate and higher response thresholds than the control participants, the response thresholds for the operated groups did not differ from the controls and the patients with right subthalamotomy had a significantly higher drift rate than unoperated patients and similar to that of controls. The drift diffusion model further established that unlike the control participants, operated patients failed to show context-dependent strategic modulation of response thresholds. The patients with right subthalamotomy could not engage in late phase, fast inhibition of the response and showed minimal proactive inhibition when tested with the contra-lesional hand. These results provide strong evidence that the subthalamic nucleus is involved in response inhibition, in modulating the rate of information accumulation and the response threshold and influencing the balance between speed and accuracy of performance. Accordingly, the subthalamic nucleus can be considered a key component of the cerebral inhibitory network.

Approximately one in five children worldwide suffers from childhood malnutrition and its complications, including increased susceptibility to inflammation and infectious diseases. Due to improved early interventions, most of these children now survive early malnutrition, even in low-resource settings (LRS). However, many continue to exhibit neurodevelopmental deficits, including low IQ, poor school performance, and behavioral problems over their lifetimes. Most studies have relied on neuropsychological tests, school performance, and mental health and behavioral measures. Few studies, in contrast, have assessed brain structure and function, and to date, these have mainly relied on low-cost techniques, including electroencephalography (EEG) and evoked potentials (ERP). The use of more advanced methods of neuroimaging, including magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), has been limited by cost factors and lack of availability of these technologies in developing countries, where malnutrition is nearly ubiquitous. This report summarizes the current state of knowledge and evidence gaps regarding childhood malnutrition and the study of its impact on neurodevelopment. It may help to inform the development of new strategies to improve the identification, classification, and treatment of neurodevelopmental disabilities in underserved populations at the highest risk for childhood malnutrition.

Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions-posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS, etc.). We emphasize that the multimodal, multiscale nature of neuroimaging data is well reflected by a multiway (tensor) structure where the underlying processes can be summarized by a relatively small number of components or “atoms.” We introduce Markov-Penrose diagrams-an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via multiway partial least squares and coupled matrix-tensor factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.

Purpose: Bone marrow stem cells (BMSC) were transplanted into the perilesional area in five patients bearing sequels of stroke, to evaluate the safety of the procedure and tolerance to the transplanted cells. Methods: Cells were obtained from bone marrow samples taken from the same patient and stereotactically implanted into the targets, determined using a combination of images, and trans-operative recording of multiunit activity. The cells were implanted in several points along tracts in the perilesional region. Results: No important adverse events derived from surgery or transplant were observed during the one year follow-up period, or detected using a combination of tests and functional measurements applied pre- and post-surgically. In contrast, some improvements were observed regarding the neurological condition of the patients, but the small number of patients in the study does not allow any conclusive statement. Conclusions: Our results demonstrate that BMSC can be safely transplanted into the brain of patients, with excellent tolerance and without complications, using the methods described here.

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64–120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca.

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.