Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method

Rehabilitation increases cortical activation during single-leg stance in patients with chronic ankle instability

Background

Chronic ankle instability (CAI) has been considered a neurophysiological disease, having as symptoms dysfunction in somatosensory and motor system excitability. Rehabilitation has been considered an effective treatment for CAI. However, few studies have explored the effects of rehabilitation on neuroplasticity in the CAI population.

Objective

The purpose of this study was to investigate the effects of rehabilitation on cortical activities for postural control in CAI patients and to find the correlation between the change in cortical activities and patient-reported outcomes (PROs).

Methods

Thirteen participants with CAI (6 female, 7 male, age = 33.8 ± 7.7 years, BMI = 24.7 ± 4.9 kg/m2) received a home exercise program for about 40 min per day, four days per week and six weeks, including ankle range-of-motion exercise, muscle strengthening, and balance activities. Cortical activation, PROs and Y-balance test outcomes were assessed and compared before and after rehabilitation. Cortical activation was detected via Functional near-infrared spectroscopy (fNIRS) while the participants performed single-leg stance tasks.

Results

The participants had better PROs and Y balance test outcomes after rehabilitation. Greater cortical activation was observed in the primary somatosensory cortex (S1, d = 0.66, p = 0.035), the superior temporal gyrus (STG, d = 1.06, p = 0.002) and the middle temporal gyrus (MTG, d = 0.66, p = 0.035) in CAI patients after rehabilitation. Moreover, significant positive correlations were observed between the recovery of ankle symptoms and the change of cortical activation in S1 (r = 0.74, p = 0.005) and STG (r = 0.72, p = 0.007) respectively.

Conclusion

The current study reveals that six weeks of rehabilitation can cause greater cortical activation in S1, STG and MTG. This increase in cortical activation suggested a better ability to perceive somatosensory stimuli and may have a compensatory role in function improvement.

Identification of Homogeneous Subgroups from Resting-State fMRI Data

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.

A multimodal neuroimaging study of brain abnormalities and clinical correlates in post treatment Lyme disease

Lyme disease is the most common vector-borne infectious disease in the United States. Post-treatment Lyme disease (PTLD) is a condition affecting 10-20% of patients in which symptoms persist despite antibiotic treatment. Cognitive complaints are common among those with PTLD, suggesting that brain changes are associated with the course of the illness. However, there has been a paucity of evidence to explain the cognitive difficulties expressed by patients with PTLD. This study administered a working memory task to a carefully screened group of 12 patients with well-characterized PTLD and 18 healthy controls while undergoing functional MRI (fMRI). A subset of 12 controls and all 12 PTLD participants also received diffusion tensor imaging (DTI) to measure white matter integrity. Clinical variables were also assessed and correlated with these multimodal MRI findings. On the working memory task, the patients with PTLD responded more slowly, but no less accurately, than did controls. FMRI activations were observed in expected regions by the controls, and to a lesser extent, by the PTLD participants. The PTLD group also hypoactivated several regions relevant to the task. Conversely, novel regions were activated by the PTLD group that were not observed in controls, suggesting a compensatory mechanism. Notably, three activations were located in white matter of the frontal lobe. DTI measures applied to these three regions of interest revealed that higher axial diffusivity correlated with fewer cognitive and neurological symptoms. Whole-brain DTI analyses revealed several frontal lobe regions in which higher axial diffusivity in the patients with PTLD correlated with longer duration of illness. Together, these results show that the brain is altered by PTLD, involving changes to white matter within the frontal lobe. Higher axial diffusivity may reflect white matter repair and healing over time, rather than pathology, and cognition appears to be dynamically affected throughout this repair process.

Redesigning navigational aids using virtual global landmarks to improve spatial knowledge retrieval

Although beacon- and map-based spatial strategies are the default strategies for navigation activities, today's navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user's sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.

i-SATA: A MATLAB based toolbox to estimate current density generated by transcranial direct current stimulation in an individual brain

OBJECTIVE

Transcranial Direct Current Stimulation (tDCS) is a technique where a weak current is passed through the electrodes placed on the scalp. The distribution of the electric current induced in the brain due to tDCS is provided by simulation toolbox like Realistic volumetric Approach based Simulator for Transcranial electric stimulation (ROAST). However, the procedure to estimate the total current density induced at the target and the intermediary region of the cortex is complex. The Systematic-Approach-for-tDCS-Analysis (SATA) was developed to overcome this problem. However, SATA is limited to standardized (MNI152) headspace only. Here we develop individual-SATA (i-SATA) to extend it to individual head.

APPROACH

T1-weighted images of 15 subjects were taken from two Magnetic Resonance Imaging scanners of different strengths. Across the subjects, the montages were simulated in ROAST. i-SATA converts the ROAST output to Talairach space. The x, y and z coordinates of the anterior commissure (AC), posterior commissure (PC), and Mid-Sagittal (MS) points are necessary for the conversion. AC and PC are detected using the acpcdetect toolbox. We developed a method to determine the MS in the image and cross-verified its location manually using BrainSight®.

MAIN RESULTS

Determination of points with i-SATA is fast and accurate. The i-SATA provided estimates of the current-density induced across an individual's cortical lobes and gyri as tested on images from two different scanners.

SIGNIFICANCE

Researchers can use i-SATA for customizing tDCS-montages. With i-SATA it is also easier to compute the inter-individual variation in current-density across the target and intermediary regions of the brain. The software is publicly available.

Three-dimensional stereotactic surface projection in the statistical analysis of single photon emission computed tomography data for distinguishing between Alzheimer’s disease and depression

AIM

To evaluate usefulness of single photon emission computed tomography (SPECT) with three-dimensional stereotactic surface projection (3D-SSP) in distinguishing between Alzheimer’s disease (AD) and depression.

METHODS

We studied 43 patients who presented with both depressive symptoms and memory disturbance. Each subject was evaluated using the following: (1) the Minimal Mental State Examination; (2) the Hamilton Rating Scale for Depression; (3) Clinical Global Impression-Severity scale (CGI-S); and (4) SPECT imaging with 3D-SSP.

RESULTS

The MMSE scores correlated significantly with the maximum Z-scores of AD-associated regions. CGI-S scores correlated significantly with the maximum Z-scores of depression-associated regions. Factor analysis identified three significant factors. Of these, Factor 1 could be interpreted as favouring a tendency for AD, Factor 2 as favouring a tendency for pseudo-dementia, and Factor 3 as favouring a depressive tendency.

CONCLUSION

We investigated whether these patients could be categorized as types: Type A (true AD), Type B (pseudo-dementia), Type C (occult AD), and Type D (true depression). The factor scores in factor analysis supported the validity of this classification. Our results suggest that SPECT with 3D-SSP is highly useful for distinguishing between depression and depressed mood in the early stage of AD.