Regular ArticleA Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development: The International Consortium for Brain Mapping (ICBM)
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Spatial correspondence of cortical activity measured with whole head fNIRS and fMRI: Toward clinical use within subject
2024, NeuroImageFunctional near infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) both measure the hemodynamic response, and so both imaging modalities are expected to have a strong correspondence in regions of cortex adjacent to the scalp. To assess whether fNIRS can be used clinically in a manner similar to fMRI, 22 healthy adult participants underwent same-day fMRI and whole-head fNIRS testing while they performed separate motor (finger tapping) and visual (flashing checkerboard) tasks. Analyses were conducted within and across subjects for each imaging approach, and regions of significant task-related activity were compared on the cortical surface. The spatial correspondence between fNIRS and fMRI detection of task-related activity was good in terms of true positive rate, with fNIRS overlap of up to 68 % of the fMRI for analyses across subjects (group analysis) and an average overlap of up to 47.25 % for individual analyses within subject. At the group level, the positive predictive value of fNIRS was 51 % relative to fMRI. The positive predictive value for within subject analyses was lower (41.5 %), reflecting the presence of significant fNIRS activity in regions without significant fMRI activity. This could reflect task-correlated sources of physiologic noise and/or differences in the sensitivity of fNIRS and fMRI measures to changes in separate (vs. combined) measures of oxy and de-oxyhemoglobin. The results suggest whole-head fNIRS as a noninvasive imaging modality with promising clinical utility for the functional assessment of brain activity in superficial regions of cortex physically adjacent to the skull.
Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers
2024, Journal of Affective DisordersMood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation.
We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories.
Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right.
The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation.
DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.
Disruptions in brain functional connectivity: The hidden risk for oxygen-intolerant professional divers in simulated deep water
2024, Biocybernetics and Biomedical EngineeringIn this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential risk of a dive and understand the physiological mechanisms involved. The results highlighted a significant difference in the baseline levels of rhythm between PD and OPD, with PD exhibiting a lower level to counteract the effects of increased inhalation, while OPD showed a higher level that resulted in a pathological state. Connectivity analysis revealed a strong correlation between cognitive and motor regions, and high levels of synchronization at rest in OPDs. Our findings suggest that a pathological condition may underlie the higher levels observed in these individuals when facing the stress of high inhalation. These findings support the hypothesis that oxygen modulates brain networks, and have important implications for understanding the neural mechanisms involved in oxygen toxicity. The study also provides a unique opportunity to investigate the impact of neurophysiological activity in simulated critical scenarios, and opens up new perspectives in the screening and monitoring of divers.
Functional connectivity of sensorimotor network is enhanced in spastic diplegic cerebral palsy: A multimodal study using fMRI and MEG
2024, Clinical NeurophysiologyTo assess the effects to functional connectivity (FC) caused by lesions related to spastic diplegic cerebral palsy (CP) in children and adolescents using multiple imaging modalities.
We used resting state magnetoencephalography (MEG) envelope signals in alpha, beta and gamma ranges and resting state functional magnetic resonance imaging (fMRI) signals to quantify FC between selected sensorimotor regions of interest (ROIs) in 11 adolescents with spastic diplegic cerebral palsy and 24 typically developing controls. Motor performance of the hands was quantified with gross motor, fine motor and kinesthesia tests.
In fMRI, participants with CP showed enhanced FC within posterior parietal regions; in MEG, they showed enhanced interhemispheric FC between sensorimotor regions and posterior parietal regions both in alpha and lower beta bands. There was a correlation between the kinesthesia score and fronto-parietal connectivity in the control population.
CP is associated with enhanced FC in sensorimotor network. This difference is not correlated with hand coordination performance. The effect of the lesion is likely not fully captured by temporal correlation of ROI signals.
Brain lesions can show as increased temporal correlation of activity between remote brain areas. We suggest this effect is likely separate from typical physiological correlates of functional connectivity.
Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain
2023, Journal of Neuroscience MethodsImaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3).
We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features.
The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition.
The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI.
The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.
Normative values of the brain health index in UK biobank
2023, Neuroimage: ReportsThe Brain Health Index (BHI) is an automated approach to quantifying brain integrity, combining different types of structural magnetic resonance imaging (MRI). Normative values derived from generally healthy individuals provide a vital baseline for understanding neurodegenerative change. Although commonplace in other areas of medicine, these are not always established when proposing new analytical approaches using MRI. The scale and quality of the UK Biobank imaging cohort (approximately N = 50k, as of 2022) allows for derivation of such values, and the wealth of additional lifestyle, physiological and demographic data enables validation of BHI through comparison with more established variables which may affect brain health.
This study aimed to: 1) establish normative BHI values in a cohort of ‘healthy’ participants, and 2) explore associations between BHI and risk factors for brain health.
The BHI was computed using voxel-based Gaussian mixture model cluster analysis of T1 and T2 FLAIR MRI in a sub-cohort of UK Biobank participants. From these data, normative score curves – with bounds described as 1, 2 and 3 standard deviations from the mean – were produced for males and females, using regression analyses to measure the scale of the BHI values as a function of age. Additional Pearson’s correlation testing was used to examine known risk factors to brain health and their relationship to BHI scores, with t-tests and ANOVAs used to determine between-group differences in BHI scoring.
Data from 2,990 participants (50.07% male, 97.05% Caucasian, 43.6% with degree-level education) were used to derive normative BHI curves from 48 to 77 years old. BHI scores were higher in female than male participants (95% CI: 0.0103 to 0.0162, p <0.001, Cohen’s d = 0.0416), males with a degree (95% CI: 0.000 to 0.009; p < 0.05; Cohen’s d = 0.044), and lower in people with type 2 diabetes mellitus (95% CI: 0.018 to 0.033; p <0.001; Cohen’s d = 0.0417), hypertension (95% CI: 0.008 to 0.018; p <0.001; Cohen’s d = 0.0419), and regular smokers (95% CI: 0.009 to 0.017, p <0.001, Cohen’s d = 0.041). BHI scores were higher in those with lower waist-to-hip ratios (WHR; males: R2 = 0.02121, F(1, 1466) = 31.77, p <0.001; females: R2 = 0.02201, F(1, 1454) = 32.72, p <0.001), and lower pulse pressure (males: R2 = 0.06261, F(1, 1215) = 81.16, p <0.001; females: R2 = 0.07616, F(1, 1205) = 99.34, p <0.001).
BHI score curves may provide useful reference values for future clinical research. More work is required to determine normative values in more diverse populations.