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PubMed1977

Regional differences of respiration in the bullfrog brain based on cell density.

The comparison of analogous and homologous regions of the anuran and mammalian brains, according to their rate of oxygen uptake per unit weight of DNA, has revealed a lack of correspondence between the respective brain regions. This discrepancy between corresponding regions was observed during incubation either in low-potassium or in high-potassium Ringer. Per unit weight of DNA, the rate of cellular respiration in the amphibian hindbrain exceeded that of the thalamus-midbrain, and cells in the latter region exceeded those in the telencephalon. Hindbrain values were only higher if based on unit weight of DNA, and were actually lower on a wet weight basis. This pattern contrasted with a uniform rate of oxygen consumption for the homologous regions of the mammalian brain. Such inequalities between the regional brain metabolism of two different classes of vertebrates clearly illustrate the unique evolutionary history of both forms. The intensity of potassium-stimulated oxygen uptake in the three brain regions of the amphibian was interpreted as probably being proportional to the number of glial cells in each area. The smallest percentage of stimulation in the hindbrain may signify fewer glia, and the highest percentage of stimulation in the mid- and forebrain may indicate more numerous glia. Histological descriptions of ependymal glia in consecutive regions of the anuran brain appear to confirm this distribution of non-neuronal cells.

Animals↗
PubMed2005

Statistical analysis of fractal-based brain tumor detection algorithms.

Fractals are geometric objects that have a noninteger fractal dimension (FD). The FD has been exploited for various biomedical recognition applications such as breast tumor and lung tumor detection. Our previous work shows that the FD is useful in the detection of brain tumors when a reference nontumor image is available. In this work, we extend our previous work by statistically validating the results of FD analysis on a set of 80 real MR and CT images. Our half-image technique requires that the tumor is located in one half of the brain whereas our whole-image technique does not. Furthermore, we alleviate the need for a reference (control) nontumor image to compute the tumor FD, which was necessary in our previous work. We also compare the brain tumor detection performance of our algorithms with other fractal-based algorithms in the literature and statistically validate our results against manually segmented tumor images. We find that the tumor region offers a statistically significant lower FD compared with that of the nontumor area for most of the FD algorithms studied in this work. Thus, our statistical analysis suggests that these FD algorithms may be exploited successfully to determine the possible presence and location of brain tumors in MR and CT images.

Algorithms↗
PubMed1998

Performance of administrators, professionals, and paraprofessionals during community-based brain injury rehabilitation training.

OBJECTIVE: Two related studies that evaluated the impact of a continuing education program about community-based rehabilitation on the performance of administrators, professionals, and paraprofessionals are presented. One study contained a second part that examined whether differences between pre-course test performance and post-course test performance might be accounted for by practice effects. DESIGN: Factorial mixed model designs. SETTING: University classroom. PARTICIPANTS: Three hundred and eight professionals, administrators, and paraprofessionals from a variety of community-based rehabilitation programs. INTERVENTION: The 4-day graduate-level course focused on three content areas: brain and behavior relationships, behavioral and cognitive intervention strategies, and a rehabilitation philosophy that emphasizes individual client rights. MAIN OUTCOME MEASURE: An examination completed before and immediately after taking the course. RESULTS: Professionals and administrators perform better than paraprofessionals when tested at the beginning and end of the training. However, the absolute differences among these groups were not substantial. In addition, the rate of learning course content was the same for administrators, paraprofessionals, and professionals. CONCLUSIONS: The results support the usefulness of training for all levels of staff and suggest that all levels of staff benefit in an equal fashion.

Administrative Personnel↗
PubMed2003

Functional magnetic resonance imaging of the human brain based on signal enhancement by extravascular protons (SEEP fMRI).

Functional magnetic resonance imaging (fMRI) studies of the human brain were carried out at 3 Tesla to investigate an fMRI contrast mechanism that does not arise from the blood oxygen-level dependent (BOLD) effect. This contrast mechanism, signal enhancement by extravascular protons (SEEP), involves only proton-density changes and was recently demonstrated to contribute to fMRI signal changes in the spinal cord. In the present study it is hypothesized that SEEP fMRI can be used to identify areas of neuronal activity in the brain with as much sensitivity and precision as can be achieved with BOLD fMRI. A detailed analysis of the areas of activity, signal intensity time courses, and the contrast-to-noise ratio (CNR), is also presented and compared with the BOLD fMRI results. Experiments were carried out with subjects performing a simple finger-touching task, or observing an alternating checkerboard pattern. Data were acquired using a conventional BOLD fMRI method (gradient-echo (GE) EPI, TE = 30 ms), a conventional method with reduced BOLD sensitivity (GE-EPI, TE = 12 ms), and SEEP fMRI (spin-echo (SE) EPI, TE = 22 ms). The results of this study demonstrate that SEEP fMRI may provide better spatial localization of areas of neuronal activity, and a higher CNR than conventional BOLD fMRI, and has the added benefit of lower sensitivity to field inhomogeneities.

Brain↗
PubMed1989

Magnetic resonance imaging-based brain morphometry: development and application to normal subjects.

A semiautomated computerized method of in vivo morphometric analysis that is based upon high-resolution three-dimensional magnetic resonance imaging has been developed. This morphometric method is efficient and is of greater analytical precision than any other morphometric method currently applied to living human tissue. Including error inherent in image data acquisition, the aggregate error of the methodology, as estimated by the phantom studies, ranges from 4.5 to 9.6%, with incremental error above 4.5% a function of magnetic resonance slice thickness. This method was applied to magnetic resonance scans of 7 normal volunteers. The derived volumes of whole brain and of individual substructures were closely concordant with previously published volumes of normal fresh (unfixed) brains obtained post mortem. This morphometric methodology is potentially applicable to any structure or lesion that can be visualized by magnetic resonance imaging.

Adolescent↗
PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗
PubMed2005

Emerging brain-based interventions for children and adolescents: overview and clinical perspective.

Electroencephalogram biofeedback (EBF), repetitive transcranial magnetic stimulation (rTMS), and vagal nerve stimulation (VNS) are emerging interventions that attempt to directly impact brain function through neurostimulation and neurofeedback mechanisms. This article provides a brief overview of each of these techniques, summarizes the relevant research findings, and examines the implications of this research for practice standards based on the guidelines for recommending evidence based treatments as developed by the American Academy of Child and Adolescent Psychiatry for attention deficit hyperactivity disorder (ADHD). EBF meets the "Clinical Guidelines" standard for ADHD, seizure disorders, anxiety, depression, and traumatic brain injury. VNS meets this same standard for treatment of refractory epilepsy and meets the lower "Options" standard for several other disorders. rTMS meets the standard for "Clinical Guidelines" for bipolar disorder, unipolar disorder, and schizophrenia. Several conditions are discussed regarding the use of evidence based thinking related to these emerging interventions and future directions.

Adolescent↗
PubMed2006

Deformation-based brain morphometry to track the course of alcoholism: differences between intra-subject and inter-subject analysis.

Substantial changes in brain morphology mark the course of alcoholism from development through dependence, recovery, and relapse. These changes can be characterized with deformation-based morphometry, which quantifies shape differences between anatomical structures, either in different subjects (cross-sectional) or in the same subject over time (longitudinal). Here we present analyses of data from a longitudinal magnetic resonance imaging (MRI) study on the effects of alcoholism on brain structure. Images were acquired from alcoholic women (n=7, mean age 47.8+/-8.3 years) and age-matched control women (n=16, mean age 51.2+/-7.5 years). From each subject, we acquired two structural MR brain images, separated by approximately 2 years (mean 21.6+/-7 months). We performed two types of morphometry using log-Jacobian maps of inter-subject and intra-subject nonrigid coordinate transformations, justified by the invariance of relevant statistics (mean, standard deviation, z-score, and t-test) under changes of the spatial and temporal reference coordinate system. With all images from one time point, a cross-sectional inter-subject morphometry determined group differences between alcoholics and normal controls. We compared these results with longitudinal intra-subject morphometry based on two images per subject acquired at different times (approximately 2 years apart). Inter- and intra-subject analysis produced partially conflicting results. Whereas the intra-subject analysis indicated faster ventricular volume increases in the alcoholics (+11% per year) than in the controls (+2% per year), the inter-subject analysis showed, on average, smaller absolute ventricle volumes in the alcoholics than in the controls (-33% relative volume). These differences were confirmed by manual planimetry and were statistically significant whether tested based on difference or change, integrated over the volume of the ventricles. Other changes and group differences were consistent between the two analyses, e.g., reduction of white matter (including corpus callosum) and increase in CSF volume, and these are in agreement with established effects of alcoholism on brain structure. We conclude that intra-subject morphometry of longitudinal data is preferable to inter-subject morphometry for detecting dynamic changes due to a disease, especially when only small samples are available. Our analysis demonstrates that the distinction between group differences observed at a point in time vs. over time is not merely academic but can substantially reduce the validity of the outcomes of actual morphometric studies. This discrepancy in results underscores the importance of distinguishing between volume differences and volume changes in morphometric analyses.

Adult↗
PubMed2006

Automated ROI-based brain parcellation analysis of frontal and temporal brain volumes in schizophrenia.

Structural MRI studies of schizophrenia have yielded a diversity of findings. To help characterize regional gray matter changes in schizophrenia, we used an automated region of interest (ROI)-based approach that targeted frontal and temporal regions in schizophrenia patients. The sample compromised 43 schizophrenia patients (21 chronic patients, 22 unmedicated first episode patients), 20 first episode non-schizophrenia psychosis patients and 47 comparison subjects. Automated regional volume measurement was performed in 22 ROIs, including frontal and temporal cortical subregions and hippocampus. Correlations between volume measures, duration of illness and clinical scores were evaluated. Chronic schizophrenia patients showed gray matter volume differences in left dorsolateral prefrontal cortex (DLPFC) and right supplementary motor area (SMA). First episode psychosis patients presented smaller right anterior cingulate cortex (ACC) and left DLPFC than comparison subjects. Disorganization scores and duration of illness correlated negatively with gray matter volume of DLPFC and SMA in chronic schizophrenia patients. Using an automated ROI-based method, we found volume reductions in lateral and medial frontal regions in both first episode and chronic schizophrenia. The automated ROI-based method can be used as a valid and efficient tool for quantification of regional gray matter volume in schizophrenia in multiple ROIs across the brains of large numbers of subjects.

Adult↗
PubMed2005

Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates.

We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.

Algorithms↗